Two-stage Audio-Visual Target Speaker Extraction System for Real-Time Processing On Edge Device

TL;DR

This paper introduces a two-stage, ultra-compact audio-visual target speaker extraction system designed for real-time processing on edge devices, combining visual cues with minimal computational load.

Contribution

It presents a novel two-stage system that significantly reduces computational complexity for real-time AVTSE on edge devices, unlike existing methods.

Findings

Effectively suppresses background noise and interference

Operates efficiently with low computational resources

Achieves real-time processing capability on edge devices

Abstract

Audio-Visual Target Speaker Extraction (AVTSE) aims to isolate a target speaker's voice in a multi-speaker environment with visual cues as auxiliary. Most of the existing AVTSE methods encode visual and audio features simultaneously, resulting in extremely high computational complexity and making it impractical for real-time processing on edge devices. To tackle this issue, we proposed a two-stage ultra-compact AVTSE system. Specifically, in the first stage, a compact network is employed for voice activity detection (VAD) using visual information. In the second stage, the VAD results are combined with audio inputs to isolate the target speaker's voice. Experiments show that the proposed system effectively suppresses background noise and interfering voices while spending little computational resources.