Single-Channel Target Speech Extraction Utilizing Distance and Room Clues

TL;DR

This paper introduces a novel single-channel target speech extraction method that incorporates distance and room environment clues, improving generalization across different acoustic settings.

Contribution

It proposes a distance and room environment embedding approach for TSE, enhancing robustness to room variations and demonstrating effectiveness on simulated and real data.

Findings

Effective in both simulated and real environments

Improves generalization across different room acoustics

Utilizes learnable embeddings for distance and room features

Abstract

This paper aims to achieve single-channel target speech extraction (TSE) in enclosures utilizing distance clues and room information. Recent works have verified the feasibility of distance clues for the TSE task, which can imply the sound source's direct-to-reverberation ratio (DRR) and thus can be utilized for speech separation and TSE systems. However, such distance clue is significantly influenced by the room's acoustic characteristics, such as dimension and reverberation time, making it challenging for TSE systems that rely solely on distance clues to generalize across a variety of different rooms. To solve this, we suggest providing room environmental information (room dimensions and reverberation time) for distance-based TSE for better generalization capabilities. Especially, we propose a distance and environment-based TSE model in the time-frequency (TF) domain with learnable distance and room embedding. Results on both simulated and real collected datasets demonstrate its feasibility. Demonstration materials are available at https://runwushi.github.io/distance-room-demo-page/.