Microphone array speech enhancement algorithm uses temporal and spatial informa- tion to improve the performance of speech noise reduction significantly. By combining noise estimation algorithm with microphone array speech enhancement, the accuracy of noise estimation is improved, and
the computation is reduced. In traditional noise es- timation algorithms, the noise power spectrum is not updated in the presence of speech, which leads to the delay and deviation of noise spectrum estimation. An optimized im- proved minimum controlled recursion average speech enhancement
algorithm, based on a microphone matrix is proposed in this paper. It consists of three parts. The first part is the preprocessing, divided into two branches: the upper branch enhances the speech signal, and the lower branch gets the noise. The second part is the optimized improved minimum
controlled recursive averaging. The noise power spectrum is updated not only in the non-speech segments but also in the speech segments. Fi- nally, according to the estimated noise power spectrum, the minimum mean-square error log-spectral amplitude algorithm is used to enhance speech. Testing
data are from TIMIT and Noisex-92 databases. Short-time objective intelligibility and seg- mental signal-to-noise ratio are chosen as evaluation metrics. Experimental results show that the proposed speech enhancement algorithm can improve the segmental signal-to-noise ratio and short-time
objective intelligibility for various noise types at different signal-to-noise ratio levels.
Wavelet speech enhancement algorithm using exponential semi-soft mask filtering
