Colored noise reduction using Bark scale spectral subtraction, statistics, and multiple time frames

A spectral subtraction algorithm is proposed for reducing colored noise from noise-corrupted speech. The spectrum is divided into frequency sub-bands based on the 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. The technique is designed to accurately detect the beginning and ending of words and brief bursts 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. Experimental results show that the algorithm removes more colored noise without removing the relatively low amplitude speech at the beginning and ending of words.