ArticleIEEE/ACM transactions on audio, speech, and language processing2020
Causal Deep CASA for Monaural Talker-Independent Speaker Separation.
Article in IEEE/ACM transactions on audio, speech, and language processing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Leveraging spatial cues from cochlear implant microphones to efficiently enhance speech separation in naturalistic listening scenes.Scientific reports · 2025Article
- DSENet: Directional Signal Extraction Network for Hearing Improvement on Edge Devices.IEEE access : practical innovations, open solutions · 2023Article
- A causal and talker-independent speaker separation/dereverberation deep learning algorithm: Cost associated with conversion to real-time capable operation.The Journal of the Acoustical Society of America · 2021Article
- A Corpus-Based Evaluation of Beamforming Techniques and Phase-Based Frequency Masking.Sensors (Basel, Switzerland) · 2021Article
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2 authors.
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Abstract
Talker-independent monaural speaker separation aims to separate concurrent speakers from a single-microphone recording. Inspired by human auditory scene analysis (ASA) mechanisms, a two-stage deep CASA approach has been proposed recently to address this problem, which achieves state-of-the-art results in separating mixtures of two or three speakers. A main limitation of deep CASA is that it is a non-causal system, while many speech processing applications, e.g., telecommunication and hearing prosthesis, require causal processing. In this study, we propose a causal version of deep CASA to address this limitation. First, we modify temporal connections, normalization and clustering algorithms in deep CASA so that no future information is used throughout the deep network. We then train a
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