| Autor: |
Zhang, Zehua, Zhang, Lu, Zhuang, Xuyi, Qian, Yukun, Wang, Mingjiang |
| Předmět: |
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| Zdroj: |
EURASIP Journal on Audio Speech & Music Processing; 4/11/2024, Vol. 2024 Issue 1, p1-16, 16p |
| Abstrakt: |
Speech signals are often distorted by reverberation and noise, with a widely distributed signal-to-noise ratio (SNR). To address this, our study develops robust, deep neural network (DNN)-based speech enhancement methods. We reproduce several DNN-based monaural speech enhancement methods and outline a strategy for constructing datasets. This strategy, validated through experimental reproductions, has effectively enhanced the denoising efficiency and robustness of the models. Then, we propose a causal speech enhancement system named Supervised Attention Multi-Scale Temporal Convolutional Network (SA-MSTCN). SA-MSTCN extracts the complex compressed spectrum (CCS) for input encoding and employs complex ratio masking (CRM) for output decoding. The supervised attention module, a lightweight addition to SA-MSTCN, guides feature extraction. Experiment results show that the supervised attention module effectively improves noise reduction performance with a minor increase in computational cost. The multi-scale temporal convolutional network refines the perceptual field and better reconstructs the speech signal. Overall, SA-MSTCN not only achieves state-of-the-art speech quality and intelligibility compared to other methods but also maintains stable denoising performance across various environments. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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