Joint filtering scheme for nonstationary noise reduction

In many state-of-the-art filtering methods for speech enhancement, an estimate of the noise statistics is required. However, the noise statistics are difficult to estimate when speech is present and, consequently, nonstationary noise has a detrimental impact on the performance of most noise reduction filters. We propose a joint filtering scheme for speech enhancement which supports the estimation of the noise statistics even during voice activity. First, we use a pitch driven linearly constrained minimum variance (LCMV) filter to estimate the noise statistics. A Wiener filter is then designed based on the estimated noise statistics, and it is applied for the noise reduction of the speech. In experiments involving real signals, we show that the proposed filtering scheme outperforms other existing speech enhancement methods in terms of perceptual evaluation of speech quality (PESQ) scores in different nonstationary noise scenarios.