2024/06/14 by Kim, Jihyun, Stijn Kindt, Kindt, Stijn +4 · 1 citation
Computer Science · #Advanced Data Compression Techniques #Audio and Speech Processing (eess.AS) #FOS: Electrical engineering #Speech Recognition and Synthesis #Speech and Audio Processing #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2406.09819
openalex publication_date 2024/06/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.