Subspace and multitaper methods for speech enhancement.

摘要

Several speech enhancement algorithms have been proposed over the years. Although most algorithms improve the quality of speech, they introduce speech distortion and suffer from the “musical noise” artifact. To minimize speech distortion, we propose subspace methods which can be generally applied for colored noise environments. To make the residual noise perceptually inaudible, we propose two methods for incorporating psychoacoustical models. In the first method, we use a well known perceptual weighting technique from speech coding to shape the residual noise spectrum. In the second method, we constrain the noise spectrum to be less than the masking threshold of the speech signal. To eliminate musical noise, we propose the use of multitaper spectrum estimators which have low variance. We further wavelet threshold the multitaper spectrum to reduce the estimation variance. For subspace methods, we propose the use of multiwindow covariance matrix estimation.; Results, based on formal listening tests and objective measures, indicated significant improvements in speech quality with the proposed algorithms. Furthermore, the proposed subspace methods yielded improved speech intelligibility when tested with cochlear implant listeners.