Year: 2024
Type: conference-paper
Abstract: Ad-hoc distributed microphone environments, where microphone locations and numbers are unpredictable, present a challenge to traditional deep learning models, which typically require fixed architectures. To tailor deep learning models to accommodate arbitrary array configurations, the Transform-Average-Concatenate (TAC) layer was previously introduced. In this work, we integrate TAC layers with dual-path transformers for speech separation from two simultaneous talkers in realistic settings. However, the distributed nature makes it hard to fuse information across microphones efficiently. Therefore, we explore the efficacy of blindly clustering microphones around sources of interest prior to enhancement. Experimental results show that this deep cluster-informed approach significantly improves the system's capacity to cope with the inherent variability observed in ad-hoc distributed microphone environments.
Source: Interspeech (USB)
Authors: Jihyun Kim, Stijn Kindt, Nilesh Madhu, Hong-Goo Kang
Language: English
Topic: Speech and Audio Processing
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4402111684