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
许韬1
作者信息
- 1. 福建林业职业技术学院 智能制造系,福建 南平 353000
- 折叠
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.
Abstract
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Key words
speech denoising/Independent Component Analysis(ICA)/gradient descent method/non-stationary引用本文复制引用
出版年
2024
NETL
NSTL
万方数据