Real-time Denoising and Dereverberation with Tiny Recurrent U-Net

• URL: http://arxiv.org/abs/2102.03207v1
• Date: Fri, 5 Feb 2021 14:46:41 GMT
• Title: Real-time Denoising and Dereverberation with Tiny Recurrent U-Net
• Authors: Hyeong-Seok Choi, Sungjin Park, Jie Hwan Lee, Hoon Heo, Dongsuk Jeon, Kyogu Lee
• Abstract summary: We propose Tiny Recurrent U-Net (TRU-Net), a lightweight online inference model that matches the performance of current state-of-the-art models. The size of the quantized version of TRU-Net is 362 kilobytes, which is small enough to be deployed on edge devices. Results of both objective and subjective evaluations have shown that our model can achieve competitive performance with the current state-of-the-art models.
• Score: 12.533488149023025
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern deep learning-based models have seen outstanding performance improvement with speech enhancement tasks. The number of parameters of state-of-the-art models, however, is often too large to be deployed on devices for real-world applications. To this end, we propose Tiny Recurrent U-Net (TRU-Net), a lightweight online inference model that matches the performance of current state-of-the-art models. The size of the quantized version of TRU-Net is 362 kilobytes, which is small enough to be deployed on edge devices. In addition, we combine the small-sized model with a new masking method called phase-aware $\beta$-sigmoid mask, which enables simultaneous denoising and dereverberation. Results of both objective and subjective evaluations have shown that our model can achieve competitive performance with the current state-of-the-art models on benchmark datasets using fewer parameters by orders of magnitude.

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