Speech denoising is the process of removing the noise from the noise corrupted speech. The applications of speech denoising are used in speech enhancement, speech recognition and many more. In this work, a new approach is proposed to de-noise the speech which is corrupted from different noises, Empirical mode decomposition and the Kalman filter (EMD-KF) is used for speech denoising in the proposed work. The clean speech is corrupted by the noise with the different SNR’s, and further Empirical mode decomposition (EMD) is applied to the noise corrupted speech later the obtained resultant speech is passed through the Kalman filter (KF) which gives the denoised speech. The result shows that the mean squared error (MSE) values of EMD-KF are extremely less when compared to other methods like discrete wavelet transform (wavelet families like Daubechies and Symlet), empirical mode decomposition (EMD) and moving average filter followed by empirical mode decomposition (MA-EMD). As an application the proposed algorithm is used in the feature extraction for speech recognition. Mel frequency cepstral coefficient (MFCC) is performed on both the original speech and the denoised speech and found majority of the denoised speech features are similar to the original speech features and few denoised speech features are nearby to the original speech features.
Empirical Mode Decomposition and Normal Shrink Tresholding for Speech Denoising
