Application of Deep Belief Network on Binaural Speech Separation and Dereverberation

• Main Authors: Chen, Yi-Ting, 陳奕廷
• Other Authors: Chi, Tai-Shih
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/34100240484476159416

碩士 === 國立交通大學 === 電信工程研究所 === 103 === Binaural speech separation and de-reverberation are popular research topics and we have developed an unsupervised clustering method for these purposes. In this thesis, we adopt a supervised classification method for binaural speech separation and de-reverberation using the ideal binary mask (IBM) as the training target and a deep belief network (DBN) as the classifier. We extract the interaural time difference (ITD) and the interaural level difference (ILD) of each T-F unit as the binaural features. To boost the performance of de-reverberation, the interaural coherence (IC) is considered when building the target IBM. We propose three different DBN architectures, the side-by-side training (monaural training), the joint training (binaural training) and the multitask learning, and compare their binaural de-reverberation performance with the performance of our previously developed unsupervised clustering method in terms of many objective criteria.