Single Channel Speech Enhancement: Using Wiener Filtering with Recursive Noise Estimation

This paper discusses the problem of single channel speech enhancement in stationary environments, and proposes Wiener filtering with the recursive noise estimation algorithm. The Wiener filter is a linear estimator and minimizes the mean-squared error between the original and enhanced speech. The algorithm is implemented in the frequency domain and depends on the filter transfer function from sample to sample based on the speech signal statistics; the local mean and the local variance. For the noise estimation, the recursive noise estimation approach is used. In this approach, the noise estimation is done by past and present spectral power values, using a smoothing parameter. The value of smoothing parameter is selected in between [0 1]. For the performance evaluation of the proposed speech enhancement algorithm objective evaluations with informal listening tests are conducted for the speech sentences, pronounced by male and female speakers from the NOIZEUS corpus, degraded by White as well as Pink noise types at different SNR levels. For objective measures, signal to noise ratio, segmental signal to noise ratio, and the perceptual evaluation of speech quality are used. The measures prove that the speech enhanced by proposed algorithm is more pleasant to the human ear for both noise conditions in comparison to the conventional speech enhancement method.