DeepASA unifies source separation, dereverberation, SED, classification, and DoAE via object-oriented processing, chain-of-inference, and temporal coherence matching, reporting SOTA on ASA2, MC-FUSS, and STARSS23.
Seld-mamba: Selective state-space model for sound event localization and detection with source distance estimation
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2025 2verdicts
UNVERDICTED 2representative citing papers
BioMamba matches Transformer performance on bioacoustics tasks while using significantly less VRAM.
citing papers explorer
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DeepASA: An Object-Oriented Multi-Purpose Network for Auditory Scene Analysis
DeepASA unifies source separation, dereverberation, SED, classification, and DoAE via object-oriented processing, chain-of-inference, and temporal coherence matching, reporting SOTA on ASA2, MC-FUSS, and STARSS23.
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State Space Models for Bioacoustics: A Comparative Evaluation with Transformers
BioMamba matches Transformer performance on bioacoustics tasks while using significantly less VRAM.