DeepASA: An Object-Oriented Multi-Purpose Network for Auditory Scene Analysis

Dongheon Lee, Younghoo Kwon, Jung-Woo Choi

Keywords: audio signal processing, auditory scene analysis, universal sound separation, object-oriented processing, chain-of-inference

TL;DR: We propose DeepASA, an object-oriented one-for-all network that mimics human auditory scene analysis to perform source separation, sound event detection, and direction-of-arrival estimation, achieving state-of-the-art performance on downstream tasks.

Abstract: We propose DeepASA, a multi-purpose 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 demo video, samples and code are available at https://huggingface.co/spaces/donghoney22/DeepASA.

Primary Area: Applications (e.g., vision, language, speech and audio, Creative AI)

Submission Number: 19503