A multi-objective learning speech enhancement algorithm based on IRM post-processing with joint estimation of SCNN and TCNN

Abstract

In this study, a novel multi-objective speech enhancement algorithm is proposed. First, we construct a deep learning architecture based on a stacked and temporal convolutional neural network (STCNN). Second, the main log-power spectra (LPS) features are input into a stacked convolutional neural network (SCNN) to extract advanced abstract features. Third, an improved power function compression Mel-frequency cepstral coefficient (PC-MFCC) feature—more consistent with human hearing characteristics than a Mel-frequency cepstral coefficient (MFCC)—is proposed. Then, a temporal convolutional neural network (TCNN) uses PC-MFCC and learned features from SCNN as input, and separately predicts a clean LPS, PC-MFCC and Ideal Ratio Mask (IRM). In this training phase, PC-MFCC constrains the LPS and IRM through a loss function to obtain the optimal network structure. Finally, IRM-based post-processing is used on the estimated clean LPS and IRM, which adjusts the weight between the above LPS and IRM to synthesise enhanced speech based on voice presence information. A series of experiments show that PC-MFCC is effective and shows complementarity with LPS in speech enhancement tasks. The proposed STCNN architecture has a higher speech enhancement performance than the comparative neural network models with good feature extraction and sequence modelling capabilities. Additionally, IRM-based post-processing further enhances the listening quality of reconstructed speech. Compared with the contrasting algorithm, the speech quality and intelligibility of enhanced speech based on the proposed multi-objective speech enhancement algorithm are further improved.