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arXiv · 2409.13285

LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement

Haoyin Yan·Jie Zhang·Cunhang Fan·Yeping Zhou·Peiqi Liu
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Abstract

Speech enhancement (SE) aims to extract the clean waveform from noise-contaminated measurements to improve the speech quality and intelligibility. Although learning-based methods can perform much better than traditional counterparts, the large computational complexity and model size heavily limit the deployment on latency-sensitive and low-resource edge devices. In this work, we propose a lightweight SE network (LiSenNet) for real-time applications. We design sub-band downsampling and upsampling blocks and a dual-path recurrent module to capture band-aware features and time-frequency patterns, respectively. A noise detector is developed to detect noisy regions in order to perform SE adaptively and save computational costs. Compared to recent higher-resource-dependent baseline models, the proposed LiSenNet can achieve a competitive performance with only 37k parameters (half of the state-of-the-art model) and 56M multiply-accumulate (MAC) operations per second.

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Haoyin Yan, Jie Zhang, Cunhang Fan, Yeping Zhou, Peiqi Liu. 2024-09-20. LiSenNet: Lightweight Sub-band and Dual-Path Modeling for Real-Time Speech Enhancement. https://arxiv.org/abs/2409.13285

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