NOISE REDUCTION USING mel-SCALE SPECTRAL SUBTRACTION WITH PERCEPTUALLY DEFINED SUBTRACTION PARAMETERS-A NEW SCHEME

The noise signal does not affect uniformly the speech signal over the whole spectrum isn the case of colored noise. In order to deal with speech improvement in such situations a new spectral subtraction algorithm is proposed for reducing colored noise from noise corrupted speech. The spectrum is divided into frequency sub-bands based on a nonlinear multiband bark scale. For each sub-band, the noise corrupted speech power in past and present time frames is compared to statistics of the noise power to improve the determination of the presence or absence of speech. During the subtraction process, a larger proportion of noise is removed from sub-bands that do not contain speech. For sub-bands that contain speech, a function is developed which allows for the removal of less noise during relatively low amplitude speech and more noise during relatively high amplitude speech .Further the performance of the spectral subtraction is improved by formulating process without neglecting the cross correlation between the speech signal and background noise. Residual noise can be masked by exploiting the masking properties of the human auditory system. In the proposed method subtraction parameters are adaptively adjusted using noise masking threshold. A psychoacoustically motivated weighting filter was included to eliminate residual musical noise. Experimental results show that the algorithm removes more colored noise without removing the relatively low amplitude speech at the beginning and ending of words.