Speech dereverberation and source separation using DNN-WPE and LWPR-PCA

Speech signals observed from distantly placed microphones may have some acoustic interference, such as noise and reverberation. These may lead to the degradation of the quality of blind speech. Hence, it is necessary to process the acquired speech signals to separate the blind source and eliminate the reverberation. Therefore, we proposed a novel speech separation and dereverberation method, which is based on the incorporation of Locally Weighted Projection Regression (LWPR)-based Principal Component Analysis (PCA) and Deep Neural Network (DNN)-based Weighted Prediction Error (WPE). The proposed method preprocesses the mixed reverberant signal prior to the application of Blind Source Separation (BSS) and Blind Dereverberation (BD). The preprocessing of the input sample signals is performed with the exploitation of fast Fourier transform (FFT) and whitening approaches to convert the time domain signal into frequency domain signal and to generate the transformation matrices. Besides, the utilization of LWPR-PCA can perform the BSS and the DNN-WPE can be used to conduct the BD. Moreover, the experimental analysis of our proposed method is compared with the existing RPCA-SNMF, CBF, BA-CNMF, AFMNMF, and ISC-LPKF approaches. The experimental outcomes depict that the proposed method effectively separates the original signal from the mixed reverberant signals.