基于独立成分分析的远场语音降噪方法研究  

Research on Far-Field Speech Noise Reduction Method Based on Principal Component Analysis

作　　者：许韬 XU Tao(Department of Intelligent Manufacturing,Fujian Vocational and Technical College of Forestry,Nanping 353000,China)

机构地区：[1]福建林业职业技术学院智能制造系,福建南平353000

出　　处：《电声技术》2024年第12期79-81,85,共4页Audio Engineering

摘　　要：研究基于独立成分分析(Independent Component Analysis,ICA)的远场语音降噪方法,并深入探讨其优化策略。首先,分析远场语音识别中噪声问题的复杂性,并探讨ICA的基本原理与应用。其次,为克服传统ICA在处理非平稳信号时的局限性,引入梯度下降法进行优化。最后,通过WSJ0-mix数据集进行测试。实验结果表明,优化后的ICA方法在信噪比上显著优于传统方法,可有效提高降噪效果。The far-field speech noise reduction method based on Independent Component Analysis(ICA) is studied,and conducts an in-depth discussion on its optimization strategy.Firstly,the complexity of the noise problem in far-field speech recognition is analyzed,and the basic principles and applications of ICA are discussed.Secondly,in order to overcome the limitations of traditional ICA in processing non-stationary signals,gradient descent method is introduced for optimization.Finally,the WSJ0-mix dataset is tested.The experimental results show that the optimized ICA method is significantly better than the traditional method in terms of signal-to-noise ratio,effectively improving the noise reduction effect.

关 键 词：语音降噪 独立成分分析(ICA) 梯度下降法 非稳态 

分 类 号：TP391.42[自动化与计算机技术—计算机应用技术]