SNR Classification Based Multi-Estimator IRM Speech Enhancement Algorithm

Abstract

Deep neural network(DNN)-based ideal ratio mask(IRM) estimation methods are often adopted in speech enhancement tasks. In the previous work, IRM estimation was usually realized by a single DNN-based IRM estimator without considering the SNR levels, which had a limited performance in real applications. Therefore, a two stage speech enhancement method is proposed in this paper. Firstly, a DNN-based SNR classifier is employed to classify the speech frames into three classes according to different SNR thresholds. Secondly, three corresponding DNN based IRM estimators related to the three SNR classes are trained respectively, from which the amplitude spectrum is corrected. Finally, speech enhancement is realized by doing IDFT to the corrected speech spectrum combined with the phase information of noisy speech. Experiment results show that the algorithm proposed in this paper has better performances in the evaluation of short time objective intelligibility(STOI), perceptual evaluation of speech quality(PESQ) and segmental signal-to-noise ratio improvement(SSNRI) scores.