Spectral Subtraction Based on Minimum Statistics

This contribution presents and analyses an algorithm for the enhancement of noisy speech signals by means of spectral subtraction. In contrast to the standard spectral subtraction algorithm the proposed method does not need a speech activity detector nor histograms to learn signal statistics. The algorithm is capable to track non stationary noise signals and compares favorably with standard spectral subtraction methods in terms of performance and computational complexity. Our noise estimation method is based on the observation that a noise power estimate can be obtained using minimum values of a smoothed power estimate of the noisy speech signal. Thus, the use of minimum statistics eliminates the problem of speech activity detection. The proposed method is conceptually simple and well suited for real time implementations. In this paper we derive an unbiased noise power estimator based on minimum statistics and discuss its statistical properties and its performance in the context of spectral subtraction.