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Performance Evaluation of PCA based Speech Enhancement Algorithm with Different Noise Estimation Method

Sangita Bavkar, Shashikant Sahare


In this paper we are presenting a speech enhancement algorithm for noisy speech signal using the principal component analysis. Principal component analysis (PCA) is a universal subspace approach which is used for enhancement of speech distorted by the noise. The principal component analysis is based on the eigenvalue analysis; the noisy speech signal eigenvalues are classified into clean speech signal eigenvalues and noisy signal eigenvalues and retaining only clean speech signal eigenvalues estimate enhanced signal. The performance evaluation of PCA based speech enhancement algorithm with different noise estimation methods are analyzed in this paper. Objective and informal listening test shows that proposed method works efficiently with improved minima controlled recursive averaging method. The system performs better noise reduction with negligible residual musical noise when tested with sentences corrupted by noise.


Eigenvalue Analysis, Noise Estimation, Noise Variance, Principal Component Analysis (PCA), Speech Enhancement, Subspace Signal

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