DNN Based Multiframe Single-Channel Noise Reduction Filters
Ningning Pan, Jingdong Chen, Jacob Benesty
Abstract
While multiframe noise reduction filters, e.g., the multiframe Wiener and minimum variance distortionless response (MVDR) ones, have demonstrated great potential to improve both the subband and full-band signal-to-noise ratios (SNRs) by exploiting explicitly the interframe speech correlation, the implementation of such filters requires the knowledge of the interframe correlation coefficients for every subband, which are challenging to estimate in practice. In this work, we present a deep neural network (DNN) based method to estimate the interframe correlation coefficients and the estimated coefficients are subsequently fed into multiframe filters to achieve noise reduction. Unlike existing DNN based methods, which outputs the enhanced speech directly, the presented method combines deep learning and traditional methods, which gives more flexibility to optimize or tune noise reduction performance. Experimental results are presented to justify the properties of the presented methods.