DeepASA: An Object-Oriented One-for-All Network for Auditory Scene Analysis
Abstract
We propose DeepASA, a one-for-all model for auditory scene analysis that performs multi-input multi-output (MIMO) source separation, dereverberation, sound event detection (SED), audio classification, and direction-of-arrival estimation (DoAE) within a unified framework. DeepASA is designed for complex auditory scenes where multiple, often similar, sound sources overlap in time and move dynamically in space. To achieve robust and consistent inference across tasks, we introduce an object-oriented processing (OOP) strategy. This approach encapsulates diverse auditory features into object-centric representations and refines them through a chain-of-inference (CoI) mechanism. The pipeline comprises a dynamic temporal kernel-based feature extractor, a transformer-based aggregator, and an object separator that yields per-object features. These features feed into multiple task-specific decoders. Our object-centric representations naturally resolve the parameter association ambiguity inherent in traditional track-wise processing. However, early-stage object separation can lead to failure in downstream ASA tasks. To address this, we implement temporal coherence matching (TCM) within the chain-of-inference, enabling multi-task fusion and iterative refinement of object features using estimated auditory parameters. We evaluate DeepASA on representative spatial audio benchmark datasets, including ASA2, MC-FUSS, and STARSS23. Experimental results show that our model achieves state-of-the-art performance across all evaluated tasks, demonstrating its effectiveness in both source separation and auditory parameter estimation under diverse spatial auditory scenes.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of DeepASA, a unified framework for auditory scene analysis that effectively integrates multiple auditory tasks through innovative object-oriented processing and a chain-of-inference mechanism. This work significantly advances the state of the art in audio processing by providing a comprehensive solution to the challenges posed by complex auditory environments.
Methodology
The proposed DeepASA framework introduces an innovative object-oriented processing (OOP) strategy that effectively encapsulates auditory features into object-centric representations, allowing for robust multi-task learning in auditory scene analysis. The integration of a chain-of-inference (CoI) mechanism to refine these representations through temporal coherence matching is a significant methodological advancement, addressing common pitfalls in traditional track-wise processing. The architecture's use of dynamic temporal kernels and transformer-based aggregators enhances its adaptability to complex auditory environments, showcasing a well-thought-out design that leverages state-of-the-art techniques in deep learning.
Results & evaluation
The experimental validation of DeepASA on multiple benchmark datasets (ASA2, MC-FUSS, and STARSS23) demonstrates its effectiveness across various tasks, achieving state-of-the-art performance metrics. The comprehensive ablation studies provide clear insights into the contributions of each component, reinforcing the robustness of the proposed architecture. However, the paper could benefit from additional comparisons with more diverse models to further contextualize its performance.
Reproducibility
The paper provides a detailed description of the architecture, training procedures, and evaluation metrics, which supports reproducibility. The availability of a demo page enhances accessibility, allowing other researchers to explore the model's capabilities. However, the lack of a public code repository may hinder full reproducibility for some practitioners.
Limitations
One notable limitation is the large parameter size associated with the ATST used in the SED decoder, which may restrict deployment in resource-constrained environments. Additionally, the reliance on specific datasets for training and evaluation may limit the generalizability of the results to other auditory scenarios.
Broader impact
The development of DeepASA has significant implications for advancing auditory scene analysis and sound separation technologies, particularly in applications such as hearing aids, surveillance systems, and interactive audio environments. By emulating human auditory processing, this research opens new avenues for improving machine understanding of complex auditory scenes, potentially benefiting various fields including robotics, virtual reality, and assistive technologies. The main contribution of this paper is the introduction of DeepASA, a unified framework for auditory scene analysis that effectively integrates multiple auditory tasks through innovative object-oriented processing and a chain-of-inference mechanism. This work significantly advances the state of the art in audio processing by providing a comprehensive solution to the challenges posed by complex auditory environments.
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