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