Research on Speech Denoising Methods Based on Deep Learning in Industrial Scenarios
This article focuses on the problem of speech signal denoising in industrial scenarios and studies a deep learning based method.Firstly,this article analyzes the characteristics of speech noise in industrial environments;Subsequently,the focus was on researching speech denoising methods based on Long Short-Term Memory(LSTM),and corresponding loss functions were constructed to improve the denoising effect.The experimental results show that the proposed method is significantly superior to the standard LSTM method in terms of reconstruction error,effectively improving the quality and clarity of speech signals.This study provides an important theoretical basis and practical method for improving the performance of speech communication systems in industrial scenarios.
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