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High Fidelity Speech Enhancement with Band-split RNN

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arxiv 2212.00406 v2 pith:KKUZGZBQ submitted 2022-12-01 eess.AS

High Fidelity Speech Enhancement with Band-split RNN

Jianwei Yu , Yi Luo , Hangting Chen , Rongzhi Gu , Chao Weng This is my paper
classification eess.AS
keywords speechband-splitbsrnndiscriminatorenhancementfull-bandhigh-frequencyquality
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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Despite the rapid progress in speech enhancement (SE) research, enhancing the quality of desired speech in environments with strong noise and interfering speakers remains challenging. In this paper, we extend the application of the recently proposed band-split RNN (BSRNN) model to full-band SE and personalized SE (PSE) tasks. To mitigate the effects of unstable high-frequency components in full-band speech, we perform bi-directional and uni-directional band-level modeling to low-frequency and high-frequency subbands, respectively. For PSE task, we incorporate a speaker enrollment module into BSRNN to utilize target speaker information. Moreover, we utilize a MetricGAN discriminator (MGD) and a multi-resolution spectrogram discriminator (MRSD) to improve perceptual quality metrics. Experimental results show that our system outperforms various top-ranking SE systems, achieves state-of-the-art (SOTA) results on the DNS-2020 test set and ranks among the top 3 in the DNS-2023 challenge.

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Cited by 2 Pith papers

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  1. PS4: Proxy-Supervised Joint Training for Real Target Speaker Extraction

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    Kimi-Audio is an open-source audio foundation model that achieves state-of-the-art results on speech recognition, audio understanding, question answering, and conversation after pre-training on more than 13 million ho...