The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate text-based reasoning by contextualizing audio content through a one-time encoding, which introduces a critical information bottleneck. Drawing inspiration from human cognition, we propose audio-interleaved reasoning to break through this bottleneck. It treats audio as an active reasoning component, enabling sustained audio engagement and perception-grounded analysis. To instantiate it, we introduce a two-stage training framework, first teaching LALMs to localize salient audio segments through supervised fine-tuning, and then incentivizing proficient re-listening via reinforcement learning. In parallel, a structured data generation pipeline is developed to produce high-quality training data. Consequently, we present Echo, a LALM capable of dynamically re-listening to audio in demand during reasoning. On audio comprehension benchmarks, Echo achieves overall superiority in both challenging expert-level and general-purpose tasks. Comprehensive analysis further confirms the efficiency and generalizability of audio-interleaved reasoning, establishing it as a promising direction for advancing audio comprehension. Project page: https://github.com/wdqqdw/Echo.
Primary: Tsinghua University
All Institutions: Tsinghua University, ByteDance China, Department of Psychological and Cognitive Sciences, School of Information Science and Technology, ShanghaiTech University
The main contribution of this paper is the introduction of audio-interleaved reasoning, which significantly enhances the audio comprehension capabilities of LALMs by allowing them to engage with audio data dynamically during reasoning tasks. This innovative approach, combined with a robust training framework and comprehensive evaluation, positions the work as a significant advancement in the field of audio machine learning.
The paper introduces a novel approach called audio-interleaved reasoning, which allows Large Audio Language Models (LALMs) to actively engage with audio data during reasoning tasks. This is achieved through a two-stage training framework that combines supervised fine-tuning and reinforcement learning, enabling the model to dynamically re-listen to salient audio segments. The methodology is well-structured, leveraging human cognitive processes as inspiration, and includes a comprehensive data generation pipeline that produces high-quality training data. The approach is innovative in its treatment of audio as an active component rather than a static context, which is a significant departure from existing methods.
The experiments are rigorously designed, utilizing multiple audio comprehension benchmarks to validate the effectiveness of the proposed methodology. The results demonstrate that Echo outperforms existing LALMs, including advanced proprietary models, in both expert-level and general-purpose tasks. The paper provides detailed comparisons and analyses, showcasing the advantages of the audio-interleaved reasoning format over traditional methods. The evaluation metrics are appropriate, and the results are statistically significant, reinforcing the claims made by the authors.
The paper includes a detailed description of the training framework, data generation pipeline, and evaluation settings, which supports reproducibility. The authors express a commitment to releasing the complete code and dataset in the future, which is crucial for enabling further research and validation of their findings.
While the proposed method shows promise, the authors acknowledge that the implementation remains relatively straightforward and that there is room for refinement. The current approach may not fully exploit the potential of audio re-listening, and the automated generation of CoT annotations lacks human heuristics, which could lead to biases in the training data. Additionally, the reliance on existing datasets may limit the generalizability of the findings.
The advancements in audio comprehension capabilities have significant implications for various applications, including human-computer interaction, accessibility technologies, and educational tools. By improving how machines understand and reason about audio, this research could lead to more intuitive and effective systems that better mimic human cognitive processes. The potential for future research in this area is substantial, particularly in enhancing the interaction between audio and other modalities. The main contribution of this paper is the introduction of audio-interleaved reasoning, which significantly enhances the audio comprehension capabilities of LALMs by allowing them to engage with audio data dynamically during reasoning tasks. This innovative approach, combined with a robust training framework and comprehensive evaluation, positions the work as a significant advancement in the field of audio machine learning.
Due to recent advancements in Large Audio-Language Models (LALMs) that demonstrate remarkable performance across a range of sound-, speech- and music-related tasks, there is a growing interest in proposing benchmarks to assess these models. Existing benchmarks generally focus only on reasoning with internal knowledge, neglecting real-world scenarios that require external information grounding. To bridge this gap, we introduce AudioRAG, a novel benchmark designed to evaluate audio-based reasoning augmented by information retrieval in realistic web environments. This benchmark comprises both LLM-generated and manually curated question-answer pairs. Our evaluations reveal that even the state-of-the-art LALMs struggle to answer these questions. We therefore propose an agentic pipeline that integrates audio reasoning with retrieval-augmented generation, providing a stronger baseline for future research.
Primary: National University of Singapore
All Institutions: National University of Singapore, The Chinese University of Hong Kong, Tianjin University
The main contribution of this paper is the introduction of AudioRAG, a benchmark for evaluating audio reasoning in conjunction with information retrieval, alongside the development of an agentic pipeline that improves performance on this benchmark. This work significantly advances the understanding of audio-Language Models' limitations and proposes a novel approach to enhance their reasoning capabilities through external knowledge integration.
The methodology is well-structured, introducing AudioRAG as a benchmark that combines audio reasoning with information retrieval. The authors employ both LLM-generated and manually curated questions, which is a thoughtful approach to ensure diversity and relevance in the dataset. The use of an agentic pipeline that integrates audio processing and retrieval-augmented generation is innovative and addresses the limitations of existing LALMs. However, the paper could benefit from more detailed descriptions of the audio processing tool and its integration with the reasoning LLM, as well as clearer explanations of the filtering process for question validity and answer correctness.
The experimental evaluation is thorough, assessing multiple state-of-the-art LALMs against the AudioRAG benchmark. The results clearly demonstrate the challenges faced by current models, highlighting the need for improved reasoning capabilities. The comparison between raw models and the agentic pipeline provides compelling evidence of the pipeline's effectiveness. However, the paper lacks detailed statistical analyses and visualizations that could further substantiate the findings.
The paper provides a GitHub repository link for the dataset, which is a positive step towards reproducibility. However, it lacks detailed implementation instructions for the agentic pipeline and the specific configurations used in experiments. This could hinder other researchers from replicating the results accurately.
One limitation is the reliance on LLMs for generating questions and answers, which may introduce biases or inaccuracies inherent in the models. Additionally, the benchmark's scope may not cover all real-world scenarios, potentially limiting its applicability. The increase in invalid answers from the agentic pipeline suggests that the complexity of multi-hop reasoning may lead to logical errors.
The proposed benchmark and agentic pipeline have significant implications for enhancing audio-based reasoning systems. By addressing the challenges of integrating external knowledge with audio processing, this work could lead to more robust applications in various fields, including education, entertainment, and information retrieval systems. The main contribution of this paper is the introduction of AudioRAG, a benchmark for evaluating audio reasoning in conjunction with information retrieval, alongside the development of an agentic pipeline that improves performance on this benchmark. This work significantly advances the understanding of audio-Language Models' limitations and proposes a novel approach to enhance their reasoning capabilities through external knowledge integration.
We present voice2mode, a method for classification of four singing phonation modes (breathy, neutral (modal), flow, and pressed) using embeddings extracted from large self-supervised speech models. Prior work on singing phonation has relied on handcrafted signal features or task-specific neural nets; this work evaluates the transferability of speech foundation models to singing phonation classification. voice2mode extracts layer-wise representations from HuBERT and two wav2vec2 variants, applies global temporal pooling, and classifies the pooled embeddings with lightweight classifiers (SVM, XGBoost). Experiments on a publicly available soprano dataset (763 sustained vowel recordings, four labels) show that foundation-model features substantially outperform conventional spectral baselines (spectrogram, mel-spectrogram, MFCC). HuBERT embeddings obtained from early layers yield the best result (~95.7% accuracy with SVM), an absolute improvement of ~12-15% over the best traditional baseline. We also show layer-wise behaviour: lower layers, which retain acoustic/phonetic detail, are more effective than top layers specialized for Automatic Speech Recognition (ASR).
Primary: Carnegie Mellon University
All Institutions: Carnegie Mellon University, University of Birmingham
This paper introduces voice2mode, a novel framework for singing voice phonation-mode classification that utilizes self-supervised speech models, demonstrating their applicability beyond traditional speech tasks. The comprehensive analysis of the methodology, experiments, and results highlights the significant advancements made in the field of audio processing and vocal analysis.
The methodology presented in this paper is robust and innovative, leveraging self-supervised learning models (HuBERT and wav2vec2) for phonation mode classification in singing. The authors effectively extract layer-wise representations from these models and apply global temporal pooling, which is a thoughtful approach to harness the strengths of deep learning architectures. The choice of classifiers (SVM and XGBoost) is appropriate given the dataset size and complexity, and the experiments are well-structured, employing a 5-fold cross-validation strategy that enhances the reliability of the results.
The experiments are comprehensive, utilizing a publicly available soprano dataset with a clear definition of phonation modes. The results demonstrate a significant improvement over traditional spectral features, with HuBERT embeddings achieving the highest accuracy. The comparative analysis with baseline features is well-presented, and the layer-wise evaluation provides valuable insights into the model's performance. However, the dataset's size (763 recordings) could limit the generalizability of the findings.
The authors have made their code publicly available, which is a strong point for reproducibility. The detailed description of the experimental setup, including data preprocessing and classifier training, further supports the ability of other researchers to replicate the study. However, the paper could benefit from more explicit details on hyperparameter tuning and the specific configurations used for the classifiers.
One limitation is the reliance on a single dataset from a single soprano singer, which may not capture the diversity of singing voices and styles. Additionally, the study focuses on a simplified set of phonation labels, which may not encompass the full range of vocal qualities present in singing. Future work should aim to include a broader dataset with varied voice types and more complex phonation categories.
The potential applications of this research are significant, particularly in the fields of vocal training and music analysis. The ability to classify phonation modes accurately could lead to the development of intelligent tools for vocal pedagogy, providing real-time feedback to singers. Furthermore, this work bridges the gap between speech and singing research, suggesting that self-supervised speech models can be effectively utilized in music information retrieval and expressive voice analysis. This paper introduces voice2mode, a novel framework for singing voice phonation-mode classification that utilizes self-supervised speech models, demonstrating their applicability beyond traditional speech tasks. The comprehensive analysis of the methodology, experiments, and results highlights the significant advancements made in the field of audio processing and vocal analysis.
Long-duration audio is increasingly common in industrial and consumer settings, yet reviewing multi-hour recordings is impractical, motivating systems that answer natural-language queries with precise temporal grounding and minimal hallucination. Existing audio-language models show promise, but long-audio question answering remains difficult due to context-length limits. We introduce LongAudio-RAG (LA-RAG), a hybrid framework that grounds Large Language Model (LLM) outputs in retrieved, timestamped acoustic event detections rather than raw audio. Multi-hour streams are converted into structured event records stored in an SQL database, and at inference time the system resolves natural-language time references, classifies intent, retrieves only the relevant events, and generates answers using this constrained evidence. To evaluate performance, we construct a synthetic long-audio benchmark by concatenating recordings with preserved timestamps and generating template-based question-answer pairs for detection, counting, and summarization tasks. Finally, we demonstrate the practicality of our approach by deploying it in a hybrid edge-cloud environment, where the audio grounding model runs on-device on IoT-class hardware while the LLM is hosted on a GPU-backed server. This architecture enables low-latency event extraction at the edge and high-quality language reasoning in the cloud. Experiments show that structured, event-level retrieval significantly improves accuracy compared to vanilla Retrieval-Augmented Generation (RAG) or text-to-SQL approaches.
Primary: unknown
All Institutions: unknown
The paper presents LongAudio-RAG, a novel framework for event-grounded question answering over multi-hour audio, significantly advancing the capabilities of audio processing systems. The detailed methodology and experimental validation underscore its potential impact in the field of machine learning, particularly in audio-language integration and real-time analytics.
The methodology presented in the paper is robust and well-structured, introducing a hybrid framework that effectively combines audio grounding with large language models (LLMs) for long audio question answering. The use of SQL databases for structured event records and the detailed approach to temporal reference resolution and intent classification are commendable. The paper clearly outlines the steps taken to convert long audio into actionable data, which is a significant advancement in the field of audio processing and natural language understanding.
The experimental evaluation is thorough, utilizing a synthetic long-audio benchmark that allows for controlled testing of the proposed system against various baselines, including RAG and text-to-SQL approaches. The results demonstrate a clear improvement in accuracy and response quality, validating the effectiveness of the proposed method. The use of both automated and human evaluations adds credibility to the findings.
The paper provides a detailed description of the implementation stack and methodologies used, which enhances reproducibility. However, the lack of a public repository or demo URL limits the ability for others to replicate the work fully. The modular service-oriented architecture described could facilitate reproducibility if made available.
The paper acknowledges limitations related to the accuracy of the Audio Grounding Model (AGM), which may affect downstream reasoning. Additionally, the synthetic nature of the benchmark may not fully capture the complexities of real-world audio environments, potentially limiting the generalizability of the results.
The proposed system has significant potential applications in various domains, including industrial monitoring, smart home technologies, and security systems. By enabling precise question answering over long audio recordings, it could enhance user interaction with audio data and improve operational efficiencies in many sectors. The paper presents LongAudio-RAG, a novel framework for event-grounded question answering over multi-hour audio, significantly advancing the capabilities of audio processing systems. The detailed methodology and experimental validation underscore its potential impact in the field of machine learning, particularly in audio-language integration and real-time analytics.
Multimodal foundation models have demonstrated impressive generalization capabilities, yet efficiently adapting them to new tasks in a few-shot setting remains a critical challenge. In this work, we investigate the few-shot adaptation of Large Audio-Language Models (ALMs) through both training-based and training-free approaches. We introduce MUKA, a multi-kernel adaptation framework that combines the fine-grained, context-dependent representations of instruction-tuning based models like Pengi with the global semantic representations of contrastive pretraining methods like CLAP. By constructing a product kernel that aligns local similarity with global semantics, MUKA enhances representational power while preserving the theoretical guarantees of kernel methods and avoiding additional training. Extensive experiments across 11 diverse audio datasets demonstrate that MUKA achieves state-of-the-art performance among training-free methods and even surpasses training-based adapters in several scenarios, offering a compelling balance between adaptability and efficiency.
Primary: IMT Atlantique
All Institutions: IMT Atlantique, Polytechnique Montréal, Inria, University Rennes, IRISA, CNRS, Université de Montpellier
The paper presents MUKA, a novel multi-kernel adaptation framework for audio-language models that enhances few-shot learning efficiency and performance. This work significantly contributes to the field by addressing the challenges of adapting large models to audio tasks, demonstrating both theoretical and practical advancements in multimodal learning.
The methodology proposed in MUKA is innovative as it introduces a multi-kernel product approach that effectively combines the strengths of different audio-language models, specifically Pengi and CLAP. This combination allows for a more nuanced representation of audio data, capturing both fine-grained details and broader semantic contexts. The theoretical grounding in kernel methods adds robustness to the approach, and the avoidance of additional training enhances its practicality in few-shot scenarios. However, the paper could benefit from a more detailed explanation of the kernel design choices and how they were empirically validated.
The experiments are extensive, covering 11 diverse audio datasets, which demonstrates the versatility of the proposed method. The results indicate that MUKA achieves state-of-the-art performance among training-free methods and competes well with training-based methods. The use of cross-validation and clear reporting of accuracy metrics strengthens the experimental rigor. However, the paper lacks a discussion on the statistical significance of the results, which would provide a clearer picture of the performance improvements.
The paper outlines the experimental setup and methodology sufficiently to allow for reproducibility. It mentions the use of specific datasets and the pre-trained models employed, along with the computational resources used for experiments. However, the absence of a public code repository or demo limits the ease of reproducibility for other researchers.
One limitation is the reliance on existing models (Pengi and CLAP) without exploring the potential for developing new models tailored specifically for audio-language tasks. Additionally, while the paper claims efficiency, it does not provide a detailed computational complexity analysis of MUKA compared to other methods. The scope of datasets, while diverse, may not cover all potential audio-language applications, which could limit the generalizability of the findings.
The implications of this work are significant for the field of audio processing and multimodal learning. By improving few-shot adaptation in audio-language models, MUKA could facilitate advancements in applications such as audio classification, emotion recognition, and sound event detection. The proposed methodology could also inspire further research into kernel methods and their applications in other domains, potentially leading to more efficient machine learning models. The paper presents MUKA, a novel multi-kernel adaptation framework for audio-language models that enhances few-shot learning efficiency and performance. This work significantly contributes to the field by addressing the challenges of adapting large models to audio tasks, demonstrating both theoretical and practical advancements in multimodal learning.
Recent Large Audio Language Models (LALMs) excel in understanding but often lack transparent reasoning. To address this "black-box" limitation, we organized the Audio Reasoning Challenge at Interspeech 2026, the first shared task dedicated to evaluating Chain-of-Thought (CoT) quality in the audio domain. The challenge introduced MMAR-Rubrics, a novel instance-level protocol assessing the factuality and logic of reasoning chains. Featured Single Model and Agent tracks, the competition attracting 156 teams from 18 countries and regions. Results show agent systems currently lead in reasoning quality, utilizing iterative tool orchestration and cross-modal analysis. Besides, single models are rapidly advancing via reinforcement learning and sophisticated data pipeline. We details the challenge design, methodology, and a comprehensive analysis of state-of-the-art systems, providing new insights for explainable audio intelligence.
Primary: Nanyang Technological University
All Institutions: Nanyang Technological University, Alibaba Group, Carnegie Mellon University, Microsoft Corporation, Queen Mary University of London, Shanghai Jiao Tong University
The paper introduces the Audio Reasoning Challenge and the MMAR-Rubrics, marking a pivotal advancement in evaluating audio reasoning models by emphasizing the quality of reasoning processes. This comprehensive analysis highlights the innovative methodology, robust experimental design, and significant implications for the field of explainable audio intelligence.
The paper presents a well-structured methodology for evaluating audio reasoning models through the introduction of the MMAR-Rubrics, which emphasizes the quality of reasoning chains rather than just final answers. This is a significant shift in evaluation paradigms, addressing the limitations of existing benchmarks that focus primarily on accuracy. The dual-track design allows for a comprehensive exploration of both end-to-end models and agent-based systems, providing insights into different architectural approaches. The use of instance-level evaluation criteria enhances the reliability and stability of the assessment process.
The experimental setup is robust, with a large number of participants (156 teams from 18 countries) demonstrating significant interest and engagement in the challenge. The results indicate a clear performance differentiation between agent systems and single models, with detailed analyses of top-performing systems providing valuable insights into effective strategies. The use of rigorous evaluation metrics, including reliability and human alignment studies, strengthens the credibility of the findings.
The paper provides sufficient details regarding the evaluation protocols and the challenge design, including the release of the MMAR-Rubrics benchmark data and evaluation scripts. However, the reproducibility of the models themselves may be limited due to the proprietary nature of some systems and the lack of detailed descriptions of their architectures and training processes.
One limitation is the potential variability in the quality of the reasoning paths generated by different models, which may not be fully captured by the evaluation metrics. Additionally, the reliance on LLMs for scoring may introduce biases or inconsistencies, although the authors have taken steps to mitigate this through their instance-level rubric approach. The challenge also does not address the scalability of the proposed evaluation methods to more complex real-world scenarios.
The findings from this research have significant implications for the development of explainable AI in audio processing, particularly in applications requiring robust reasoning capabilities, such as automated transcription services, audio analysis for accessibility, and interactive audio agents. By focusing on the reasoning process, this work contributes to enhancing the transparency and trustworthiness of AI systems in critical domains. The paper introduces the Audio Reasoning Challenge and the MMAR-Rubrics, marking a pivotal advancement in evaluating audio reasoning models by emphasizing the quality of reasoning processes. This comprehensive analysis highlights the innovative methodology, robust experimental design, and significant implications for the field of explainable audio intelligence.
Bengali (Bangla) remains under-resourced in long-form speech technology despite its wide use. We present Bengali-Loop, two community benchmarks to address this gap: (1) a long-form ASR corpus of 191 recordings (158.6 hours, 792k words) from 11 YouTube channels, collected via a reproducible subtitle-extraction pipeline and human-in-the-loop transcript verification; and (2) a speaker diarization corpus of 24 recordings (22 hours, 5,744 annotated segments) with fully manual speaker-turn labels in CSV format. Both benchmarks target realistic multi-speaker, long-duration content (e.g., Bangla drama/natok). We establish baselines (Tugstugi: 34.07% WER; pyannote.audio: 40.08% DER) and provide standardized evaluation protocols (WER/CER, DER), annotation rules, and data formats to support reproducible benchmarking and future model development for Bangla long-form ASR and diarization.
Primary: unknown
All Institutions: unknown
The paper presents Bengali-Loop, a significant contribution to the field of speech technology for the Bengali language, providing essential benchmarks for long-form ASR and speaker diarization. The methodology is sound, and the technical contributions are likely to foster further advancements in this under-resourced area, although some limitations and areas for improvement remain.
The methodology presented in the paper is robust, focusing on the collection and verification of long-form ASR and speaker diarization datasets. The use of a human-in-the-loop approach for transcript verification enhances the quality of the data, addressing common pitfalls in automated transcription. The standardized evaluation protocols and formats provided are essential for reproducibility and future research. However, the paper could benefit from a more detailed discussion on the specific challenges encountered during data collection and annotation, as well as the rationale behind the chosen methodologies.
The experimental evaluation is thorough, with clear baselines established for both ASR and diarization tasks. The reported results, including WER and DER, provide a solid foundation for assessing the performance of the proposed benchmarks. However, the paper lacks a comparative analysis with existing benchmarks in other languages, which could further contextualize the results and demonstrate the significance of the contributions made.
The authors emphasize reproducibility by providing detailed descriptions of the data collection process, annotation guidelines, and evaluation protocols. They also plan to release scripts for standardizing audio and running baseline evaluations, which is commendable. However, the lack of a publicly available code repository limits the ease with which other researchers can reproduce the results.
The paper acknowledges several limitations, including the limited dialectal diversity of the datasets and the simplification of the diarization overlap policy. Additionally, the focus on specific types of media (e.g., Bangla drama) may not fully represent the diversity of spoken Bengali in other contexts. These limitations should be addressed in future work to enhance the applicability of the benchmarks.
The development of Bengali-Loop has significant implications for the advancement of speech technology in under-resourced languages. By providing high-quality datasets and standardized evaluation protocols, this work can facilitate further research and development in Bangla ASR and speaker diarization. The benchmarks can also serve as a foundation for community-driven efforts to improve speech technology for other low-resource languages, potentially leading to broader accessibility and inclusion in technology. The paper presents Bengali-Loop, a significant contribution to the field of speech technology for the Bengali language, providing essential benchmarks for long-form ASR and speaker diarization. The methodology is sound, and the technical contributions are likely to foster further advancements in this under-resourced area, although some limitations and areas for improvement remain.
We present Eureka-Audio, a compact yet high-performance audio language model that achieves competitive performance against models that are 4 to 18 times larger across a broad range of audio understanding benchmarks. Despite containing only 1.7B parameters, Eureka-Audio demonstrates strong performance on automatic speech recognition (ASR), audio understanding, and dense audio captioning, matching or surpassing multiple 7B to 30B audio and omni-modal baselines. The model adopts a unified end-to-end architecture composed of a lightweight language backbone, a Whisper-based audio encoder, and a sparsely activated Mixture-of-Experts (MoE) adapter that explicitly accounts for audio heterogeneity and alleviates cross-modal optimization conflicts under limited capacity. To further enhance paralinguistic reasoning, we introduce DataFlux, a closed loop audio instruction data synthesis and verification pipeline that constructs high quality, logically consistent supervision from raw audio. Extensive evaluations across ASR, knowledge reasoning, safety, instruction following, and paralinguistic benchmarks, demonstrate that Eureka-Audio achieves an efficient balance between computational cost and performance. These results establish Eureka Audio as a strong and practical baseline for lightweight audio understanding models.
Primary: Inner Mongolia University
All Institutions: Baidu Inc., College of Computer Science, Inner Mongolia University, Tsinghua Shenzhen International Graduate School, Tsinghua University
The main contribution of this paper is the introduction of Eureka-Audio, a compact audio language model that achieves competitive performance against much larger models while employing innovative techniques for audio understanding and data synthesis. This work represents a meaningful advancement in the field of audio processing, particularly in developing efficient models that maintain high performance.
The methodology presented in the paper is robust, featuring a unified end-to-end architecture that integrates a lightweight language backbone with a Whisper-based audio encoder and a Mixture-of-Experts (MoE) adapter. This approach effectively addresses audio heterogeneity and cross-modal optimization conflicts, which are common challenges in audio processing tasks. The introduction of the DataFlux pipeline for synthesizing and verifying audio instruction data is particularly innovative, as it enhances the model's ability to reason about paralinguistic features. The model's architecture is well-justified, and the combination of techniques appears to be a significant advancement in the field of audio language models.
The experimental evaluation is comprehensive, covering a wide range of benchmarks including ASR, audio understanding, and dense audio captioning. The results demonstrate that Eureka-Audio outperforms or matches larger models, which is a significant achievement given its compact size of 1.7B parameters. The paper provides detailed comparisons with various baselines, and the metrics used for evaluation are appropriate and well-explained. However, the lack of real-world application scenarios in the experiments could limit the practical understanding of the model's performance.
The paper includes a project URL that suggests the availability of code and models, which is crucial for reproducibility. However, the paper does not provide extensive details on the training procedures, hyperparameters, or datasets used, which could hinder full reproducibility by other researchers. More transparency in these areas would enhance the paper's contribution to the community.
One limitation of the study is the potential overfitting to the benchmarks used for evaluation, as the model's performance is primarily reported on standard datasets. Additionally, the reliance on a closed-loop data synthesis approach may introduce biases or limitations in the quality of the generated data. The paper could also explore the model's performance in diverse real-world scenarios beyond the controlled benchmarks.
Eureka-Audio has the potential to significantly impact various applications in audio understanding, including accessibility technologies, voice-activated systems, and interactive AI agents. Its compact size makes it suitable for deployment in resource-constrained environments, which could broaden the accessibility of advanced audio processing capabilities. The advancements in paralinguistic reasoning could also lead to more nuanced interactions in human-computer communication. The main contribution of this paper is the introduction of Eureka-Audio, a compact audio language model that achieves competitive performance against much larger models while employing innovative techniques for audio understanding and data synthesis. This work represents a meaningful advancement in the field of audio processing, particularly in developing efficient models that maintain high performance.
We present voice2mode, a method for classification of four singing phonation modes (breathy, neutral (modal), flow, and pressed) using embeddings extracted from large self-supervised speech models. Prior work on singing phonation has relied on handcrafted signal features or task-specific neural nets; this work evaluates the transferability of speech foundation models to singing phonation classification. voice2mode extracts layer-wise representations from HuBERT and two wav2vec2 variants, applies global temporal pooling, and classifies the pooled embeddings with lightweight classifiers (SVM, XGBoost). Experiments on a publicly available soprano dataset (763 sustained vowel recordings, four labels) show that foundation-model features substantially outperform conventional spectral baselines (spectrogram, mel-spectrogram, MFCC). HuBERT embeddings obtained from early layers yield the best result (~95.7% accuracy with SVM), an absolute improvement of ~12-15% over the best traditional baseline. We also show layer-wise behaviour: lower layers, which retain acoustic/phonetic detail, are more effective than top layers specialized for Automatic Speech Recognition (ASR).
Primary: Carnegie Mellon University
All Institutions: Carnegie Mellon University, University of Birmingham
This paper introduces voice2mode, a novel framework for singing voice phonation-mode classification that utilizes self-supervised speech models, demonstrating their applicability beyond traditional speech tasks. The comprehensive analysis of the methodology, experiments, and results highlights the significant advancements made in the field of audio processing and vocal analysis.
The methodology presented in this paper is robust and innovative, leveraging self-supervised learning models (HuBERT and wav2vec2) for phonation mode classification in singing. The authors effectively extract layer-wise representations from these models and apply global temporal pooling, which is a thoughtful approach to harness the strengths of deep learning architectures. The choice of classifiers (SVM and XGBoost) is appropriate given the dataset size and complexity, and the experiments are well-structured, employing a 5-fold cross-validation strategy that enhances the reliability of the results.
The experiments are comprehensive, utilizing a publicly available soprano dataset with a clear definition of phonation modes. The results demonstrate a significant improvement over traditional spectral features, with HuBERT embeddings achieving the highest accuracy. The comparative analysis with baseline features is well-presented, and the layer-wise evaluation provides valuable insights into the model's performance. However, the dataset's size (763 recordings) could limit the generalizability of the findings.
The authors have made their code publicly available, which is a strong point for reproducibility. The detailed description of the experimental setup, including data preprocessing and classifier training, further supports the ability of other researchers to replicate the study. However, the paper could benefit from more explicit details on hyperparameter tuning and the specific configurations used for the classifiers.
One limitation is the reliance on a single dataset from a single soprano singer, which may not capture the diversity of singing voices and styles. Additionally, the study focuses on a simplified set of phonation labels, which may not encompass the full range of vocal qualities present in singing. Future work should aim to include a broader dataset with varied voice types and more complex phonation categories.
The potential applications of this research are significant, particularly in the fields of vocal training and music analysis. The ability to classify phonation modes accurately could lead to the development of intelligent tools for vocal pedagogy, providing real-time feedback to singers. Furthermore, this work bridges the gap between speech and singing research, suggesting that self-supervised speech models can be effectively utilized in music information retrieval and expressive voice analysis. This paper introduces voice2mode, a novel framework for singing voice phonation-mode classification that utilizes self-supervised speech models, demonstrating their applicability beyond traditional speech tasks. The comprehensive analysis of the methodology, experiments, and results highlights the significant advancements made in the field of audio processing and vocal analysis.
Large Audio Language Models (LALMs) excel at perception but struggle with complex reasoning requiring precise acoustic measurements. While external tools can extract fine-grained features like exact tempo or pitch, effective integration remains challenging: naively using all tools causes information overload, while prompt-based selection fails to assess context-dependent utility. To address this, we propose AuTAgent (Audio Tool Agent), a reinforcement learning framework that learns when and which tools to invoke. By employing a sparse-feedback training strategy with a novel Differential Reward mechanism, the agent learns to filter out irrelevant tools and invokes external assistance only when it yields a net performance gain over the base model. Experimental results confirm that AuTAgent complements the representation bottleneck of LALMs by providing verifiable acoustic evidence. It improves accuracy by 4.20% / 6.20% and 9.80% / 8.00% for open-source and closed-source backbones on the MMAU Test-mini and the MMAR benchmarks, respectively. In addition, further experiments demonstrate exceptional transferability. We highlight the complementary role of external tools in augmenting audio model reasoning.
Primary: Institute of Acoustics, Chinese Academy of Sciences
All Institutions: Institute of Acoustics, Chinese Academy of Sciences, Institute of Computing Technology, Chinese Academy of Sciences, The University of Queensland, University of Chinese Academy of Sciences, University of California, Merced
The main contribution of this paper is the introduction of AuTAgent, a reinforcement learning framework that enhances audio reasoning by intelligently selecting and invoking external tools, thereby addressing the representation bottleneck in existing audio models. This work represents a substantial advancement in the integration of reinforcement learning with audio processing, offering a novel approach to improve reasoning accuracy and efficiency in complex audio tasks.
The methodology presented in AuTAgent is innovative, leveraging reinforcement learning to optimize tool selection for audio reasoning tasks. The introduction of a Baseline-Subtracted Differential Reward mechanism is particularly noteworthy, as it addresses the challenge of tool redundancy and noise interference effectively. The use of Group Relative Policy Optimization (GRPO) allows the agent to learn from performance feedback dynamically, which is a significant improvement over traditional static prompting methods. The paper clearly articulates the problem of representation bottlenecks in Large Audio Language Models (LALMs) and proposes a structured approach to mitigate these issues through active tool invocation.
The experimental evaluation is robust, utilizing two well-defined benchmarks (MMAU Test-mini and MMAR) to assess the performance of AuTAgent against various baselines. The reported performance improvements (4.20% to 9.80% across different models) substantiate the effectiveness of the proposed framework. The experiments also demonstrate the transferability of the learned tool-selection policy across different reasoning backbones, which is a strong indicator of the approach's generalizability.
The paper provides sufficient implementation details, including the training setup, dataset construction, and evaluation metrics, which enhances reproducibility. However, the lack of a publicly available code repository or demo limits the practical reproducibility of the results, as external researchers cannot directly validate the findings without access to the implementation.
One limitation of the study is the reliance on a relatively small training dataset (approximately 2,000 samples), which may affect the generalization capabilities of the AuTAgent in more complex real-world scenarios. Additionally, while the paper addresses the noise introduced by improper tool integration, it does not explore the potential computational overhead associated with invoking multiple tools, which could be a concern in resource-constrained environments.
The implications of this work are significant for the field of audio processing and reasoning, as it opens avenues for more effective integration of external tools in LALMs. The ability to enhance reasoning capabilities through strategic tool usage could lead to advancements in various applications, including audio analysis, music information retrieval, and environmental sound classification. This research could also inspire further exploration into reinforcement learning applications in multimodal reasoning tasks beyond audio. The main contribution of this paper is the introduction of AuTAgent, a reinforcement learning framework that enhances audio reasoning by intelligently selecting and invoking external tools, thereby addressing the representation bottleneck in existing audio models. This work represents a substantial advancement in the integration of reinforcement learning with audio processing, offering a novel approach to improve reasoning accuracy and efficiency in complex audio tasks.
As deepfake audio becomes more realistic and diverse, developing generalizable countermeasure systems has become crucial. Existing detection methods primarily depend on XLS-R front-end features to improve generalization. Nonetheless, their performance remains limited, partly due to insufficient attention to fine-grained information, such as physiological cues or frequency-domain features. In this paper, we propose BreathNet, a novel audio deepfake detection framework that integrates fine-grained breath information to improve generalization. Specifically, we design BreathFiLM, a feature-wise linear modulation mechanism that selectively amplifies temporal representations based on the presence of breathing sounds. BreathFiLM is trained jointly with the XLS-R extractor, in turn encouraging the extractor to learn and encode breath-related cues into the temporal features. Then, we use the frequency front-end to extract spectral features, which are then fused with temporal features to provide complementary information introduced by vocoders or compression artifacts. Additionally, we propose a group of feature losses comprising Positive-only Supervised Contrastive Loss (PSCL), center loss, and contrast loss. These losses jointly enhance the discriminative ability, encouraging the model to separate bona fide and deepfake samples more effectively in the feature space. Extensive experiments on five benchmark datasets demonstrate state-of-the-art (SOTA) performance. Using the ASVspoof 2019 LA training set, our method attains 1.99% average EER across four related eval benchmarks, with particularly strong performance on the In-the-Wild dataset, where it achieves 4.70% EER. Moreover, under the ASVspoof5 evaluation protocol, our method achieves an EER of 4.94% on this latest benchmark.
Primary: Institute of Automation, Chinese Academy of Sciences
All Institutions: Institute of Automation, Chinese Academy of Sciences, Sun Yat-sen University, Guangdong Key Laboratory of Information Security, China Mobile Communications Corporation
The main contribution of this paper is the development of BreathNet, an innovative audio deepfake detection framework that leverages breath-related features and a dual-branch architecture to achieve state-of-the-art performance. This comprehensive analysis highlights the technical contributions, methodological advancements, and potential impact of the research in addressing the growing challenges posed by deepfake audio technologies.
The proposed BreathNet framework introduces a novel approach to audio deepfake detection by integrating breath-related cues into the feature extraction process. The BreathFiLM module effectively modulates temporal features based on detected breath sounds, enhancing the model's ability to differentiate between genuine and synthetic audio. Additionally, the dual-branch architecture that combines temporal and frequency-domain features through cross-attention is a significant methodological advancement. The use of a carefully designed feature loss that includes PSCL, center loss, and contrast loss further refines the model's discriminative capabilities. Overall, the methodology is well-structured and innovative, addressing key limitations in existing detection systems.
The experiments conducted on five benchmark datasets demonstrate the robustness and generalization capabilities of the proposed method. The reported state-of-the-art results, including a 1.99% average EER on the ASVspoof 2019 LA training set and 4.70% on the In-the-Wild dataset, validate the effectiveness of the proposed approach. The ablation studies provide insights into the contributions of individual components, reinforcing the importance of breath cues and the feature loss design. However, the paper could benefit from more extensive comparisons with a broader range of existing methods to contextualize its performance further.
The paper provides detailed implementation details, including the architecture, training procedures, and hyperparameters used, which are essential for reproducibility. However, the absence of a publicly available code repository or demo URL limits the ease of reproducing the results. Including a GitHub link or similar would enhance the paper's impact and facilitate further research.
One limitation is the reliance on breath detection, which may not be universally applicable across all audio samples, particularly those with significant background noise or non-human speech. Additionally, while the model shows strong performance on benchmark datasets, its effectiveness in real-world scenarios with diverse audio conditions remains to be thoroughly evaluated. The paper could also explore the computational efficiency of the proposed method, as the complexity of the BreathFiLM module may impact real-time applications.
The implications of this research are significant, particularly in the context of security and trust in voice communication technologies. As deepfake audio becomes more prevalent, the ability to detect such manipulations is crucial for protecting biometric systems and maintaining the integrity of voice-based interactions. The proposed method has the potential to enhance security measures in various applications, including online authentication and digital forensics. The main contribution of this paper is the development of BreathNet, an innovative audio deepfake detection framework that leverages breath-related features and a dual-branch architecture to achieve state-of-the-art performance. This comprehensive analysis highlights the technical contributions, methodological advancements, and potential impact of the research in addressing the growing challenges posed by deepfake audio technologies.
Spoofing-robust automatic speaker verification (SASV) seeks to build automatic speaker verification systems that are robust against both zero-effort impostor attacks and sophisticated spoofing techniques such as voice conversion (VC) and text-to-speech (TTS). In this work, we propose a novel SASV architecture that introduces score-aware gated attention (SAGA), SASV-SAGA, enabling dynamic modulation of speaker embeddings based on countermeasure (CM) scores. By integrating speaker embeddings and CM scores from pre-trained ECAPA-TDNN and AASIST models respectively, we explore several integration strategies including early, late, and full integration. We further introduce alternating training for multi-module (ATMM) and a refined variant, evading alternating training (EAT). Experimental results on the ASVspoof 2019 Logical Access (LA) and Spoofceleb datasets demonstrate significant improvements over baselines, achieving a spoofing aware speaker verification equal error rate (SASV-EER) of 1.22% and minimum normalized agnostic detection cost function (min a-DCF) of 0.0304 on the ASVspoof 2019 evaluation set. These results confirm the effectiveness of score-aware attention mechanisms and alternating training strategies in enhancing the robustness of SASV systems.
Primary: Ben-Gurion University of the Negev
All Institutions: Ben-Gurion University of the Negev, Afeka Academic College of Engineering
The main contribution of this paper is the introduction of a novel SASV architecture that leverages score-aware gated attention and alternating training strategies to improve robustness against spoofing attacks. This work significantly advances the field of speaker verification by providing a comprehensive framework that integrates speaker embeddings with countermeasure scores, demonstrating substantial performance improvements on established benchmarks.
The paper presents a robust methodology for spoofing-robust automatic speaker verification (SASV) through the introduction of score-aware gated attention (SAGA) and alternating training for multi-module (ATMM) strategies. The integration of speaker embeddings and countermeasure scores using various fusion strategies (early, late, and full integration) is well-structured, allowing for dynamic modulation based on the countermeasure scores. The evading alternating training (EAT) mechanism is a novel adaptation that addresses the challenges of domain mismatch during training, enhancing the model's robustness against unseen spoofing attacks. The methodology is theoretically sound and grounded in existing literature, providing a solid foundation for the proposed techniques.
The experimental evaluation is comprehensive, utilizing two well-established datasets (ASVspoof 2019 and SpoofCeleb) to validate the proposed methods. The results demonstrate significant improvements over baseline models, with the proposed ELEAT-SAGA model achieving an impressive SASV-EER of 1.22% and a min a-DCF of 0.0304. The paper provides a thorough analysis of different training approaches and integration strategies, showcasing the effectiveness of the proposed methods in enhancing performance and generalization. However, the statistical significance of the improvements could be more explicitly discussed.
While the methodology is detailed, the paper lacks explicit implementation details that would facilitate reproducibility. Key aspects such as hyperparameter settings, specific training configurations, and code availability are not provided. Including a link to a code repository or supplementary materials would greatly enhance reproducibility.
The paper does not address potential limitations in the generalization of the proposed methods to other datasets or real-world scenarios outside of the evaluated benchmarks. Additionally, the reliance on pre-trained models (ECAPA-TDNN and AASIST) may limit the applicability of the approach if these models do not perform well in different contexts. The impact of the evading mechanism (EAT) on model performance could also be further explored.
The advancements in SASV systems presented in this paper have significant implications for biometric authentication and security applications, particularly in combating sophisticated spoofing attacks. The proposed methods could be adapted for various applications in voice recognition, security systems, and user authentication processes, enhancing the reliability and robustness of speaker verification systems in real-world scenarios. The main contribution of this paper is the introduction of a novel SASV architecture that leverages score-aware gated attention and alternating training strategies to improve robustness against spoofing attacks. This work significantly advances the field of speaker verification by providing a comprehensive framework that integrates speaker embeddings with countermeasure scores, demonstrating substantial performance improvements on established benchmarks.
Recent advances in speech language models, such as GPT-4o Voice Mode and Gemini Live, have demonstrated promising speech generation capabilities. Nevertheless, the aesthetic naturalness of the synthesized audio still lags behind that of human speech. Enhancing generation quality requires a reliable evaluator of speech naturalness. However, existing naturalness evaluators typically regress raw audio to scalar scores, offering limited interpretability of the evaluation and moreover fail to generalize to speech across different taxonomies. Inspired by recent advances in generative reward modeling, we propose the Generative Speech Reward Model (GSRM), a reasoning-centric reward model tailored for speech. The GSRM is trained to decompose speech naturalness evaluation into an interpretable acoustic feature extraction stage followed by feature-grounded chain-of-thought reasoning, enabling explainable judgments. To achieve this, we curated a large-scale human feedback dataset comprising 31k expert ratings and an out-of-domain benchmark of real-world user-assistant speech interactions. Experiments show that GSRM substantially outperforms existing speech naturalness predictors, achieving model-human correlation of naturalness score prediction that approaches human inter-rater consistency. We further show how GSRM can improve the naturalness of speech LLM generations by serving as an effective verifier for online RLHF.
Primary: unknown
All Institutions: unknown
The main contribution of this paper is the introduction of GSRM, a novel model that enhances speech naturalness evaluation through a combination of acoustic feature extraction and reasoning-based assessments. This approach not only improves the accuracy of naturalness predictions but also provides interpretability, which is crucial for advancing the field of speech synthesis and reinforcement learning from human feedback.
The proposed Generative Speech Reward Model (GSRM) introduces a novel approach by integrating acoustic feature extraction with reasoning-based evaluations, which enhances interpretability in assessing speech naturalness. The methodology is well-structured, utilizing a large-scale dataset of expert ratings that strengthens the model's training process. However, the paper could benefit from a more detailed description of the feature extraction techniques and the reasoning framework employed.
The experiments are robust, demonstrating GSRM's performance against existing models. The use of a substantial dataset (31k expert ratings) and an out-of-domain benchmark provides a solid foundation for the results. The reported model-human correlation metrics are promising, suggesting that GSRM effectively captures human-like evaluations of speech naturalness. However, more diverse testing scenarios could further validate the model's generalizability.
The paper lacks sufficient implementation details, such as specific algorithms used for feature extraction and the training process. Without sharing code or a clear methodology, reproducibility may be challenging for other researchers. Providing a GitHub repository or supplementary materials would significantly enhance this aspect.
One limitation is the reliance on expert ratings, which may not fully represent the broader population's perception of speech naturalness. Additionally, the model's performance in real-world applications remains to be thoroughly tested, as the current benchmarks are limited to specific datasets.
The GSRM has the potential to significantly improve the quality of synthesized speech in various applications, including virtual assistants, audiobooks, and accessibility tools. By enhancing the naturalness of generated speech, it could lead to more engaging user experiences and broader acceptance of AI-generated audio content. The main contribution of this paper is the introduction of GSRM, a novel model that enhances speech naturalness evaluation through a combination of acoustic feature extraction and reasoning-based assessments. This approach not only improves the accuracy of naturalness predictions but also provides interpretability, which is crucial for advancing the field of speech synthesis and reinforcement learning from human feedback.
Deep Neural Networks (DNNs) often struggle to suppress noise at low signal-to-noise ratios (SNRs). This paper addresses speech enhancement in scenarios dominated by harmonic noise and proposes a framework that integrates cyclostationarity-aware preprocessing with lightweight DNN-based denoising. A cyclic minimum power distortionless response (cMPDR) spectral beamformer is used as a preprocessing block. It exploits the spectral correlations of cyclostationary noise to suppress harmonic components prior to learning-based enhancement and does not require modifications to the DNN architecture. The proposed pipeline is evaluated in a single-channel setting using two DNN architectures: a simple and lightweight convolutional recurrent neural network (CRNN), and a state-of-the-art model, namely ultra-low complexity network (ULCNet). Experiments on synthetic data and real-world recordings dominated by rotating machinery noise demonstrate consistent improvements over end-to-end DNN baselines, particularly at low SNRs. Remarkably, a parameter-efficient CRNN with cMPDR preprocessing surpasses the performance of the larger ULCNet operating on raw or Wiener-filtered inputs. These results indicate that explicitly incorporating cyclostationarity as a signal prior is more effective than increasing model capacity alone for suppressing harmonic interference.
Primary: Delft University of Technology
All Institutions: Delft University of Technology, Bang & Olufsen
This paper presents a novel hybrid framework for speech enhancement that effectively combines cyclostationarity-aware preprocessing with DNN-based denoising, showcasing significant performance improvements in low-SNR scenarios. The methodology is well-supported by rigorous experimentation, and the findings could have substantial implications for real-world applications in noisy environments.
The proposed methodology effectively integrates cyclostationarity-aware preprocessing with DNN-based denoising, utilizing a cyclic minimum power distortionless response (cMPDR) beamformer to enhance speech in low-SNR environments. This two-step approach is innovative as it leverages the unique properties of cyclostationary noise without necessitating modifications to the DNN architecture, thus maintaining a lightweight model. The choice of using both a simple convolutional recurrent neural network (CRNN) and a more complex ultra-low complexity network (ULCNet) for evaluation provides a robust comparison of the method's effectiveness across different model complexities.
The experimental evaluation is thorough, employing both synthetic and real-world datasets to demonstrate the method's effectiveness. The results consistently show significant improvements in performance metrics such as SI-SDR and DNSMOS, particularly in low-SNR conditions. The paper clearly delineates the performance gains achieved through the proposed preprocessing step, establishing a strong case for the benefits of incorporating cyclostationarity in speech enhancement tasks.
The paper provides sufficient implementation details, including architecture specifications, training protocols, and hyperparameters, which facilitate reproducibility. The availability of the code on GitHub further enhances the potential for other researchers to replicate the study and build upon the findings.
One limitation noted is the reliance on stable noise frequencies for the cMPDR to be effective, which may not hold in all real-world scenarios. Additionally, the method's performance on non-cyclostationary noise types could be less effective, as indicated by the results on the DNS dataset.
The proposed approach has significant implications for applications in industrial environments where effective speech communication is crucial amidst high levels of noise. By improving speech enhancement technologies, this work could enhance the usability of hearing aids and communication devices in challenging acoustic conditions, potentially benefiting a wide range of users. This paper presents a novel hybrid framework for speech enhancement that effectively combines cyclostationarity-aware preprocessing with DNN-based denoising, showcasing significant performance improvements in low-SNR scenarios. The methodology is well-supported by rigorous experimentation, and the findings could have substantial implications for real-world applications in noisy environments.
We present a decoder-only Conformer for automatic speech recognition (ASR) that processes speech and text in a single stack without external speech encoders or pretrained large language models (LLM). The model uses a modality-aware sparse mixture of experts (MoE): disjoint expert pools for speech and text with hard routing and top-1 selection, embedded in hybrid-causality Conformer blocks (bidirectional for speech, causal for text). Training combines CTC on speech positions with label-smoothed cross-entropy for text generation. Our 113M-parameter model consistently improves WER over a 139M AED baseline on Librispeech (2.8% vs. 3.2% test-clean; 5.6% vs. 6.0% test-other). On Common Voice 16.1 with a single multilingual model across five languages, our approach reduces average WER from 12.2% to 10.6%. To our knowledge, this is the first randomly initialized decoder-only ASR that surpasses strong AED baselines via modality-aware routing and sparse MoE, achieving better accuracy with fewer active parameters and without alignment/adaptation modules.
Primary: unknown
All Institutions: unknown
This paper presents a decoder-only Conformer architecture that effectively integrates modality-aware sparse mixtures of experts for automatic speech recognition. The innovative approach and solid experimental results position it as a valuable contribution to the field, although further work is needed to enhance reproducibility and address practical deployment challenges.
The paper introduces a novel decoder-only Conformer architecture that integrates modality-aware sparse mixtures of experts (MoE) for automatic speech recognition (ASR). The methodology is well-structured, leveraging a single stack to process both speech and text without the need for external encoders or pretrained models. The use of disjoint expert pools for speech and text, along with hard routing and top-1 selection, is innovative and addresses the challenge of heterogeneous modality integration effectively. The hybrid causality approach is also a significant contribution, allowing for bidirectional processing of speech while maintaining causal generation for text. However, the paper could benefit from a more detailed explanation of the routing mechanism and its implications on model performance.
The experiments are robust, demonstrating consistent improvements in word error rates (WER) over strong baselines across multiple datasets, including Librispeech and Common Voice 16.1. The results validate the proposed model's effectiveness, showing that it can outperform traditional encoder-decoder architectures while maintaining a lower parameter count. The comparative analysis against various baselines is thorough, but additional ablation studies could further clarify the contributions of individual components, such as the modality-aware routing and load-balancing loss.
The paper provides sufficient implementation details, including model configurations, training epochs, and data augmentation techniques, which facilitate reproducibility. However, the absence of a publicly available code repository or demo limits the ability for other researchers to replicate the results independently. Including a link to the code would significantly enhance the paper's reproducibility.
While the proposed model shows promising results, it relies on a relatively complex architecture that may pose challenges in practical deployment scenarios, especially in real-time applications. Additionally, the paper does not address the potential computational overhead introduced by the MoE mechanism, which may affect inference speed. Future work should also consider the scalability of the model to larger datasets and more diverse languages.
The research has significant implications for the field of automatic speech recognition, particularly in unifying speech and text processing within a single framework. This could lead to more efficient and effective ASR systems, especially in multilingual contexts. The approach may also inspire further research into modality-aware architectures in other domains, such as natural language processing and computer vision. This paper presents a decoder-only Conformer architecture that effectively integrates modality-aware sparse mixtures of experts for automatic speech recognition. The innovative approach and solid experimental results position it as a valuable contribution to the field, although further work is needed to enhance reproducibility and address practical deployment challenges.
The maturation of Large Audio Language Models (LALMs) has raised growing expectations for them to comprehend complex audio much like humans. Current efforts primarily replicate text-based reasoning by contextualizing audio content through a one-time encoding, which introduces a critical information bottleneck. Drawing inspiration from human cognition, we propose audio-interleaved reasoning to break through this bottleneck. It treats audio as an active reasoning component, enabling sustained audio engagement and perception-grounded analysis. To instantiate it, we introduce a two-stage training framework, first teaching LALMs to localize salient audio segments through supervised fine-tuning, and then incentivizing proficient re-listening via reinforcement learning. In parallel, a structured data generation pipeline is developed to produce high-quality training data. Consequently, we present Echo, a LALM capable of dynamically re-listening to audio in demand during reasoning. On audio comprehension benchmarks, Echo achieves overall superiority in both challenging expert-level and general-purpose tasks. Comprehensive analysis further confirms the efficiency and generalizability of audio-interleaved reasoning, establishing it as a promising direction for advancing audio comprehension. Project page: https://github.com/wdqqdw/Echo.
Primary: Tsinghua University
All Institutions: Tsinghua University, ByteDance China, Department of Psychological and Cognitive Sciences, School of Information Science and Technology, ShanghaiTech University
The main contribution of this paper is the introduction of audio-interleaved reasoning, which significantly enhances the audio comprehension capabilities of LALMs by allowing them to engage with audio data dynamically during reasoning tasks. This innovative approach, combined with a robust training framework and comprehensive evaluation, positions the work as a significant advancement in the field of audio machine learning.
The paper introduces a novel approach called audio-interleaved reasoning, which allows Large Audio Language Models (LALMs) to actively engage with audio data during reasoning tasks. This is achieved through a two-stage training framework that combines supervised fine-tuning and reinforcement learning, enabling the model to dynamically re-listen to salient audio segments. The methodology is well-structured, leveraging human cognitive processes as inspiration, and includes a comprehensive data generation pipeline that produces high-quality training data. The approach is innovative in its treatment of audio as an active component rather than a static context, which is a significant departure from existing methods.
The experiments are rigorously designed, utilizing multiple audio comprehension benchmarks to validate the effectiveness of the proposed methodology. The results demonstrate that Echo outperforms existing LALMs, including advanced proprietary models, in both expert-level and general-purpose tasks. The paper provides detailed comparisons and analyses, showcasing the advantages of the audio-interleaved reasoning format over traditional methods. The evaluation metrics are appropriate, and the results are statistically significant, reinforcing the claims made by the authors.
The paper includes a detailed description of the training framework, data generation pipeline, and evaluation settings, which supports reproducibility. The authors express a commitment to releasing the complete code and dataset in the future, which is crucial for enabling further research and validation of their findings.
While the proposed method shows promise, the authors acknowledge that the implementation remains relatively straightforward and that there is room for refinement. The current approach may not fully exploit the potential of audio re-listening, and the automated generation of CoT annotations lacks human heuristics, which could lead to biases in the training data. Additionally, the reliance on existing datasets may limit the generalizability of the findings.
The advancements in audio comprehension capabilities have significant implications for various applications, including human-computer interaction, accessibility technologies, and educational tools. By improving how machines understand and reason about audio, this research could lead to more intuitive and effective systems that better mimic human cognitive processes. The potential for future research in this area is substantial, particularly in enhancing the interaction between audio and other modalities. The main contribution of this paper is the introduction of audio-interleaved reasoning, which significantly enhances the audio comprehension capabilities of LALMs by allowing them to engage with audio data dynamically during reasoning tasks. This innovative approach, combined with a robust training framework and comprehensive evaluation, positions the work as a significant advancement in the field of audio machine learning.
Accurate upsampling of Head-Related Transfer Functions (HRTFs) from sparse measurements is crucial for personalized spatial audio rendering. Traditional interpolation methods, such as kernel-based weighting or basis function expansions, rely on measurements from a single subject and are limited by the spatial sampling theorem, resulting in significant performance degradation under sparse sampling. Recent learning-based methods alleviate this limitation by leveraging cross-subject information, yet most existing neural architectures primarily focus on modeling spatial relationships across directions, while spectral dependencies along the frequency dimension are often modeled implicitly or treated independently. However, HRTF magnitude responses exhibit strong local continuity and long-range structure in the frequency domain, which are not fully exploited. This work investigates frequency-domain feature modeling by examining how different architectural choices, ranging from per-frequency multilayer perceptrons to convolutional, dilated convolutional, and attention-based models, affect performance under varying sparsity levels, showing that explicit spectral modeling consistently improves reconstruction accuracy, particularly under severe sparsity. Motivated by this observation, a frequency-domain Conformer-based architecture is adopted to jointly capture local spectral continuity and long-range frequency correlations. Experimental results on the SONICOM and HUTUBS datasets demonstrate that the proposed method achieves state-of-the-art performance in terms of interaural level difference and log-spectral distortion.
Primary: University of Technology Sydney
All Institutions: University of Technology Sydney, Monash University
This paper makes a substantial contribution to the field of audio processing by introducing a frequency-domain modeling approach for HRTF magnitude upsampling, demonstrating its effectiveness through rigorous experimentation and analysis. The findings highlight the importance of architectural choices in modeling spectral features, paving the way for future innovations in personalized audio rendering.
The paper proposes a novel approach to HRTF magnitude upsampling through frequency-domain feature modeling. It critically examines various architectural choices, including per-frequency MLPs, convolutional models, and a Conformer-based architecture, to effectively capture both local spectral continuity and long-range frequency correlations. The methodology is well-structured, with a clear separation between spatial mapping and frequency-domain modeling, which allows for a comprehensive exploration of the design space. The integration of spectral gradient loss alongside log-spectral distortion as a training objective is a thoughtful addition that enhances the model's ability to preserve spectral features.
The experiments are robust, utilizing two well-established datasets (SONICOM and HUTUBS) to evaluate the proposed method's performance under varying sparsity levels. The results demonstrate that the FD-Conformer consistently outperforms existing methods in terms of interaural level difference (ILD) and log-spectral distortion (LSD), particularly in sparse measurement scenarios. The ablation studies provide valuable insights into the contributions of different components of the architecture, reinforcing the importance of frequency-domain modeling.
The paper includes sufficient details regarding the experimental setup, including the datasets used, preprocessing steps, model architecture, and training protocols. The availability of the source code on GitHub enhances reproducibility, allowing other researchers to validate and build upon the findings.
While the proposed method shows significant improvements, it may still be sensitive to the choice of hyperparameters and the specific configurations of the datasets used. Additionally, the performance in extremely sparse scenarios, while improved, may still not meet practical requirements for all applications, indicating a potential area for further research.
The advancements in HRTF upsampling have significant implications for personalized spatial audio rendering, which is increasingly relevant in virtual reality, gaming, and immersive audio applications. By improving the accuracy of HRTF estimations from sparse measurements, this research could enhance user experiences in various audio applications, making spatial audio more accessible and effective. This paper makes a substantial contribution to the field of audio processing by introducing a frequency-domain modeling approach for HRTF magnitude upsampling, demonstrating its effectiveness through rigorous experimentation and analysis. The findings highlight the importance of architectural choices in modeling spectral features, paving the way for future innovations in personalized audio rendering.
Although lip-to-speech synthesis (L2S) has achieved significant progress in recent years, current state-of-the-art methods typically rely on intermediate representations such as mel-spectrograms or discrete self-supervised learning (SSL) tokens. The potential of latent diffusion models (LDMs) in this task remains largely unexplored. In this paper, we introduce SLD-L2S, a novel L2S framework built upon a hierarchical subspace latent diffusion model. Our method aims to directly map visual lip movements to the continuous latent space of a pre-trained neural audio codec, thereby avoiding the information loss inherent in traditional intermediate representations. The core of our method is a hierarchical architecture that processes visual representations through multiple parallel subspaces, initiated by a subspace decomposition module. To efficiently enhance interactions within and between these subspaces, we design the diffusion convolution block (DiCB) as our network backbone. Furthermore, we employ a reparameterized flow matching technique to directly generate the target latent vectors. This enables a principled inclusion of speech language model (SLM) and semantic losses during training, moving beyond conventional flow matching objectives and improving synthesized speech quality. Our experiments show that SLD-L2S achieves state-of-the-art generation quality on multiple benchmark datasets, surpassing existing methods in both objective and subjective evaluations.
Primary: unknown
All Institutions: unknown
The paper presents SLD-L2S, a novel framework for high-fidelity lip-to-speech synthesis that leverages a hierarchical subspace latent diffusion model, achieving state-of-the-art results in synthesis quality. The methodology is innovative and addresses critical challenges in the field, while the experimental evaluation supports its effectiveness, though the lack of a publicly available implementation may hinder reproducibility.
The paper introduces a novel framework, SLD-L2S, which employs a hierarchical subspace latent diffusion model to directly map visual lip movements to the latent space of a pre-trained audio codec. The methodology is innovative in its use of diffusion convolution blocks (DiCB) and a reparameterized flow matching technique, which enhances the model's ability to generate high-fidelity speech without relying on traditional intermediate representations like mel-spectrograms. The hierarchical architecture and subspace decomposition approach are well-justified, addressing the inherent challenges of lip-to-speech synthesis effectively.
The experiments are robust, utilizing multiple benchmark datasets (LRS3-TED and LRS2-BBC) to validate the performance of the proposed method. The results demonstrate that SLD-L2S achieves state-of-the-art performance in both objective and subjective evaluations, significantly outperforming existing methods. The use of comprehensive metrics, including UTMOS, SCOREQ, WER, and subjective MOS tests, provides a well-rounded assessment of the model's capabilities.
The paper provides detailed implementation details, including architecture configurations, training procedures, and hyperparameter settings, which are essential for reproducibility. However, the absence of a publicly available code repository or demo URL limits the practical reproducibility of the results.
One notable limitation is the lack of a clear discussion on the potential computational costs associated with the proposed method, particularly in real-world applications. Additionally, the paper does not address the scalability of the model to different languages or accents, which could impact its generalizability.
The proposed SLD-L2S framework has significant implications for various applications, including automated video dubbing, assistive technologies for individuals with speech impairments, and enhancing communication in noisy environments. By improving the quality and intelligibility of synthesized speech from visual inputs, this work could facilitate more natural interactions in human-computer interfaces. The paper presents SLD-L2S, a novel framework for high-fidelity lip-to-speech synthesis that leverages a hierarchical subspace latent diffusion model, achieving state-of-the-art results in synthesis quality. The methodology is innovative and addresses critical challenges in the field, while the experimental evaluation supports its effectiveness, though the lack of a publicly available implementation may hinder reproducibility.
Due to recent advancements in Large Audio-Language Models (LALMs) that demonstrate remarkable performance across a range of sound-, speech- and music-related tasks, there is a growing interest in proposing benchmarks to assess these models. Existing benchmarks generally focus only on reasoning with internal knowledge, neglecting real-world scenarios that require external information grounding. To bridge this gap, we introduce AudioRAG, a novel benchmark designed to evaluate audio-based reasoning augmented by information retrieval in realistic web environments. This benchmark comprises both LLM-generated and manually curated question-answer pairs. Our evaluations reveal that even the state-of-the-art LALMs struggle to answer these questions. We therefore propose an agentic pipeline that integrates audio reasoning with retrieval-augmented generation, providing a stronger baseline for future research.
Primary: National University of Singapore
All Institutions: National University of Singapore, The Chinese University of Hong Kong, Tianjin University
The main contribution of this paper is the introduction of AudioRAG, a benchmark for evaluating audio reasoning in conjunction with information retrieval, alongside the development of an agentic pipeline that improves performance on this benchmark. This work significantly advances the understanding of audio-Language Models' limitations and proposes a novel approach to enhance their reasoning capabilities through external knowledge integration.
The methodology is well-structured, introducing AudioRAG as a benchmark that combines audio reasoning with information retrieval. The authors employ both LLM-generated and manually curated questions, which is a thoughtful approach to ensure diversity and relevance in the dataset. The use of an agentic pipeline that integrates audio processing and retrieval-augmented generation is innovative and addresses the limitations of existing LALMs. However, the paper could benefit from more detailed descriptions of the audio processing tool and its integration with the reasoning LLM, as well as clearer explanations of the filtering process for question validity and answer correctness.
The experimental evaluation is thorough, assessing multiple state-of-the-art LALMs against the AudioRAG benchmark. The results clearly demonstrate the challenges faced by current models, highlighting the need for improved reasoning capabilities. The comparison between raw models and the agentic pipeline provides compelling evidence of the pipeline's effectiveness. However, the paper lacks detailed statistical analyses and visualizations that could further substantiate the findings.
The paper provides a GitHub repository link for the dataset, which is a positive step towards reproducibility. However, it lacks detailed implementation instructions for the agentic pipeline and the specific configurations used in experiments. This could hinder other researchers from replicating the results accurately.
One limitation is the reliance on LLMs for generating questions and answers, which may introduce biases or inaccuracies inherent in the models. Additionally, the benchmark's scope may not cover all real-world scenarios, potentially limiting its applicability. The increase in invalid answers from the agentic pipeline suggests that the complexity of multi-hop reasoning may lead to logical errors.
The proposed benchmark and agentic pipeline have significant implications for enhancing audio-based reasoning systems. By addressing the challenges of integrating external knowledge with audio processing, this work could lead to more robust applications in various fields, including education, entertainment, and information retrieval systems. The main contribution of this paper is the introduction of AudioRAG, a benchmark for evaluating audio reasoning in conjunction with information retrieval, alongside the development of an agentic pipeline that improves performance on this benchmark. This work significantly advances the understanding of audio-Language Models' limitations and proposes a novel approach to enhance their reasoning capabilities through external knowledge integration.
Large Audio Language Models (LALMs) have demonstrated strong capabilities in audio understanding and reasoning. However, their performance on fine grained auditory perception remains unreliable, and existing approaches largely rely on data intensive training to internalize perceptual abilities. We propose AudioRouter, a reinforcement learning framework that enables LALMs to improve audio understanding by learning when and how to use external audio tools. Rather than tightly coupling tool usage with audio reasoning, AudioRouter formulates tool use as an explicit decision making problem and optimizes a lightweight routing policy while keeping the underlying reasoning model frozen. Experimental results show that AudioRouter achieves substantial improvements on standard audio understanding benchmarks while requiring up to 600x less training data to learn tool usage compared with conventional training paradigms. These findings suggest that learning effective tool usage offers a data efficient and scalable alternative to internalizing perceptual abilities in LALMs.
Primary: University of California
All Institutions: University of California, The University of Queensland
The main contribution of this paper is the introduction of AudioRouter, a reinforcement learning framework that enhances audio understanding in large audio language models by optimizing tool usage while significantly reducing the amount of required training data. This innovative approach not only improves performance but also offers a scalable alternative to traditional data-intensive training methods, marking a significant advancement in the field of audio processing and reasoning.
The methodology presented in the paper is innovative as it decouples tool usage from the reasoning model, allowing for a more efficient learning process. The use of reinforcement learning to optimize a routing policy for tool invocation is a significant departure from traditional end-to-end training approaches. The authors effectively formulate tool usage as a discrete decision-making problem, which is a novel perspective in the context of audio language models. The decision to keep the reasoning model frozen while training the router is a strategic choice that enhances data efficiency and reduces complexity.
The experimental evaluation is robust, demonstrating the effectiveness of AudioRouter across multiple benchmarks (MMAU-mini and MMAR). The results indicate substantial improvements in performance while requiring significantly less training data compared to conventional methods. The paper provides clear comparisons against baseline models, showcasing the advantages of the proposed framework. However, the experiments could benefit from a broader range of datasets and tasks to further validate the generalizability of the approach.
The paper includes sufficient details regarding the experimental setup, including model architectures, training data, and reinforcement learning specifics. However, the lack of URLs for code or project repositories limits the reproducibility of the results. Providing access to the trained models or implementation would enhance the ability of other researchers to replicate the findings.
The paper acknowledges that the relative outcome reward relies on a fixed reasoning model, which may limit the Router's learning signal. Additionally, the focus on short-form, closed-set audio reasoning tasks with a limited set of audio tools may restrict the applicability of the findings. Future work should explore extending the framework to more complex reasoning tasks and diverse tool capabilities.
The proposed AudioRouter framework has the potential to significantly advance the field of audio understanding by providing a more data-efficient method for leveraging external tools. This approach could lead to broader applications in various domains, including audio analysis, multimedia processing, and interactive AI systems. By reducing the reliance on large annotated datasets, it may also democratize access to advanced audio processing capabilities. The main contribution of this paper is the introduction of AudioRouter, a reinforcement learning framework that enhances audio understanding in large audio language models by optimizing tool usage while significantly reducing the amount of required training data. This innovative approach not only improves performance but also offers a scalable alternative to traditional data-intensive training methods, marking a significant advancement in the field of audio processing and reasoning.
Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches often rely on pretrained encoders, semantic distillation, or heterogeneous CNN-based architectures. These designs introduce fixed inductive biases that limit reconstruction fidelity and hinder effective scaling. In this paper, we argue that discrete audio tokenization should be learned fully end-to-end using a homogeneous and scalable architecture. To this end, we first propose CAT (Causal Audio Tokenizer with Transformer), a purely Transformer-based architecture that jointly optimizes the encoder, quantizer, and decoder from scratch for high-fidelity reconstruction. Building on the CAT architecture, we develop MOSS-Audio-Tokenizer, a large-scale audio tokenizer featuring 1.6 billion parameters, pre-trained on 3 million hours of diverse, general audio data. We show that this simple, fully end-to-end approach built from homogeneous, causal Transformer blocks scales gracefully and supports high-fidelity reconstruction across diverse audio domains. Across speech, sound, and music, MOSS-Audio-Tokenizer consistently outperforms prior codecs over a wide range of bitrates, while exhibiting predictable improvements with increased scale. Notably, leveraging the discrete tokens from our model, we develop the first purely autoregressive TTS model that surpasses prior non-autoregressive and cascaded systems. Furthermore, MOSS-Audio-Tokenizer enables competitive ASR performance without auxiliary encoders. Our findings position the CAT architecture as a unified, scalable interface for the next generation of native audio foundation models.
Primary: Fudan University
All Institutions: Fudan University, MOSI Intelligence, Shanghai Innovation Institute
The paper presents MOSS-Audio-Tokenizer, a novel end-to-end audio tokenizer that significantly improves audio processing capabilities for autoregressive models. Its comprehensive methodology and robust experimental validation establish it as a noteworthy contribution to the field of machine learning and audio processing.
The paper introduces the Causal Audio Tokenizer (CAT), a novel architecture that employs a fully end-to-end approach to audio tokenization using a homogeneous stack of causal Transformer blocks. This design minimizes fixed inductive biases, allowing for high-fidelity audio reconstruction across diverse domains. The architecture's simplicity and scalability are emphasized, with joint optimization of the encoder, quantizer, decoder, and discriminator, which is a significant departure from existing methods that often rely on pretrained components or complex architectures. The methodology is well-structured, with clear explanations of the training objectives and the integration of semantic modeling through audio-to-text tasks.
The authors conduct extensive experiments to evaluate the performance of MOSS-Audio-Tokenizer against existing audio tokenizers across various bitrate regimes. The results demonstrate state-of-the-art reconstruction quality in speech, sound, and music, with a clear advantage in low-bitrate scenarios. The use of both objective and subjective evaluation metrics strengthens the findings, providing a comprehensive assessment of the model's capabilities. The experiments are well-designed, showcasing the effectiveness of the proposed Progressive Sequence Dropout training strategy and the model's robustness across different conditions.
The paper provides detailed implementation information, including architecture specifications, training schedules, and optimization strategies. However, it lacks a publicly accessible code repository or demo URL, which could hinder reproducibility. The absence of shared code or datasets limits the ability for other researchers to validate the findings independently.
While the paper presents a strong technical contribution, it does not sufficiently address potential limitations, such as the computational resources required for training the large-scale model and the generalizability of the results to real-world applications. Additionally, the reliance on a large dataset for training may not be feasible for all researchers.
The development of MOSS-Audio-Tokenizer has significant implications for the field of audio processing and generation, particularly in enhancing the capabilities of autoregressive models. Its ability to provide high-fidelity audio reconstruction and support various downstream tasks like text-to-speech and automatic speech recognition positions it as a valuable tool for future audio foundation models. The research could lead to advancements in applications such as virtual assistants, content creation, and accessibility technologies. The paper presents MOSS-Audio-Tokenizer, a novel end-to-end audio tokenizer that significantly improves audio processing capabilities for autoregressive models. Its comprehensive methodology and robust experimental validation establish it as a noteworthy contribution to the field of machine learning and audio processing.
Discrete audio tokenizers are fundamental to empowering large language models with native audio processing and generation capabilities. Despite recent progress, existing approaches often rely on pretrained encoders, semantic distillation, or heterogeneous CNN-based architectures. These designs introduce fixed inductive biases that limit reconstruction fidelity and hinder effective scaling. In this paper, we argue that discrete audio tokenization should be learned fully end-to-end using a homogeneous and scalable architecture. To this end, we first propose CAT (Causal Audio Tokenizer with Transformer), a purely Transformer-based architecture that jointly optimizes the encoder, quantizer, and decoder from scratch for high-fidelity reconstruction. Building on the CAT architecture, we develop MOSS-Audio-Tokenizer, a large-scale audio tokenizer featuring 1.6 billion parameters, pre-trained on 3 million hours of diverse, general audio data. We show that this simple, fully end-to-end approach built from homogeneous, causal Transformer blocks scales gracefully and supports high-fidelity reconstruction across diverse audio domains. Across speech, sound, and music, MOSS-Audio-Tokenizer consistently outperforms prior codecs over a wide range of bitrates, while exhibiting predictable improvements with increased scale. Notably, leveraging the discrete tokens from our model, we develop the first purely autoregressive TTS model that surpasses prior non-autoregressive and cascaded systems. Furthermore, MOSS-Audio-Tokenizer enables competitive ASR performance without auxiliary encoders. Our findings position the CAT architecture as a unified, scalable interface for the next generation of native audio foundation models.
Primary: Fudan University
All Institutions: Fudan University, MOSI Intelligence, Shanghai Innovation Institute
The paper introduces MOSS-Audio-Tokenizer, a scalable and effective audio tokenizer that leverages a fully end-to-end Transformer architecture to achieve high-fidelity audio reconstruction and competitive performance in downstream tasks. This work represents a significant advancement in audio processing methodologies, emphasizing the importance of simplicity and scalability in model design.
The paper presents a novel architecture, MOSS-Audio-Tokenizer, built on the Causal Audio Tokenizer (CAT) framework, which utilizes a purely Transformer-based approach for audio tokenization. This end-to-end model optimizes the encoder, quantizer, and decoder jointly, which is a significant departure from existing methods that rely on pretrained encoders or complex architectures. The use of residual vector quantization and a multi-task learning strategy to align audio representations with text further enhances the methodology. The design principles emphasize simplicity, scalability, and causality, making it suitable for autoregressive modeling.
The experiments are comprehensive, evaluating the model across various audio domains including speech, sound, and music. The authors provide both objective and subjective metrics for reconstruction quality, demonstrating that MOSS-Audio-Tokenizer consistently outperforms existing codecs across different bitrates. The results indicate a clear advantage in reconstruction fidelity and robustness, particularly in low-bitrate scenarios, showcasing the effectiveness of the proposed architecture.
The paper includes detailed implementation specifics, including architecture configurations, training schedules, and optimization strategies. However, the lack of a publicly available code repository or demo limits the reproducibility of the results. The authors do mention training on a substantial dataset (3 million hours of audio), but without access to the code or data, independent verification of results could be challenging.
One limitation is the reliance on a large-scale dataset for training, which may not be readily available to all researchers. Additionally, while the model shows strong performance across various tasks, the scalability of the architecture in real-world applications and its performance in edge cases or less common audio types remains to be fully explored.
The MOSS-Audio-Tokenizer has the potential to significantly advance the field of audio processing by providing a unified framework for audio generation and understanding. Its applications could extend to various domains including speech synthesis, automatic speech recognition, and audio content generation, making it a valuable tool for both researchers and practitioners in the field. The paper introduces MOSS-Audio-Tokenizer, a scalable and effective audio tokenizer that leverages a fully end-to-end Transformer architecture to achieve high-fidelity audio reconstruction and competitive performance in downstream tasks. This work represents a significant advancement in audio processing methodologies, emphasizing the importance of simplicity and scalability in model design.
Standardized laboratory characterizations for absorbing materials rely on idealized sound field assumptions, which deviate largely from real-life conditions. Consequently, \emph{in-situ} acoustic characterization has become essential for accurate diagnosis and virtual prototyping. We propose a physics-informed neural field that reconstructs local, near-surface broadband sound fields from sparse pressure samples to directly infer complex surface impedance. A parallel, multi-frequency architecture enables a broadband impedance retrieval within runtimes on the order of seconds to minutes. To validate the method, we developed a compact microphone array with low hardware complexity. Numerical verifications and laboratory experiments demonstrate accurate impedance retrieval with a small number of sensors under realistic conditions. We further showcase the approach in a vehicle cabin to provide practical guidance on measurement locations that avoid strong interference. Here, we show that this approach offers a robust means of characterizing \emph{in-situ} boundary conditions for architectural and automotive acoustics.
Primary: Technical University of Denmark
All Institutions: Technical University of Denmark
The main contribution of this paper is the development of a physics-informed neural network framework for rapid in-situ characterization of surface impedance from sparse acoustic data, which significantly advances the state-of-the-art in acoustic material characterization. The methodology combines innovative neural network architecture with practical experimental validation, addressing critical challenges in the field of acoustics.
The paper introduces a novel physics-informed neural network architecture for inferring surface impedance from sparse acoustic data, which is a significant advancement over traditional methods that rely on dense sensor arrays and idealized conditions. The use of a parallel multi-frequency architecture allows for efficient processing and inference, addressing computational bottlenecks associated with broadband sound field reconstruction. The methodology is well-structured, incorporating automatic differentiation to infer particle velocity, and employs a composite loss function that integrates data fidelity, physical constraints, and regularization terms, which enhances the robustness of the model.
The experimental validation is thorough, encompassing both numerical simulations and laboratory experiments in anechoic and reverberant environments. The results demonstrate the framework's capability to accurately retrieve impedance under realistic conditions, showcasing its practical applicability in complex acoustic environments such as vehicle cabins. The sensitivity analysis and parametric sweeps provide valuable insights into the performance of the proposed microphone array configurations, further reinforcing the robustness of the method.
The paper provides detailed descriptions of the experimental setups, training protocols, and evaluation metrics, which facilitate reproducibility. However, the lack of publicly available code and data at this stage may hinder independent validation of the results. The authors mention plans to establish a public repository upon acceptance, which would enhance reproducibility.
One limitation noted is the sensitivity of the method to local sound field complexity, particularly in the presence of strong nodal lines and reflections, which can degrade inference accuracy. Additionally, the reliance on specific microphone configurations may limit the generalizability of the findings to other setups or environments. The paper also acknowledges the challenges posed by measurement noise, especially in the context of near-rigid surfaces.
The proposed framework has significant implications for in-situ acoustic characterization in various fields, including architectural acoustics and automotive design. By enabling rapid and accurate impedance retrieval, this method can improve the design and optimization of sound-absorbing materials and structures, ultimately enhancing acoustic performance in real-world applications. The integration of machine learning with physics-informed approaches represents a promising direction for future research in acoustic engineering. The main contribution of this paper is the development of a physics-informed neural network framework for rapid in-situ characterization of surface impedance from sparse acoustic data, which significantly advances the state-of-the-art in acoustic material characterization. The methodology combines innovative neural network architecture with practical experimental validation, addressing critical challenges in the field of acoustics.