| Summary: | 碩士 === 國立交通大學 === 電信工程研究所 === 106 === De-reverberation and De-noise to cancel the reverberant effect and environment noise has always been an important task in speech processing, however, time-domain deconvolution algorithms often require a series of complicated pro-cesses and provide no good results. In this thesis, we propose de-reverberation and de-noise algorithms in the temporal modulation domain using a machine learning technique. Inspired by human auditory processing, the time domain convolution operation was first transformed to the temporal modulation domain and a deep neural network (DNN) was used to learn how to de-reverberate and de-noise speech signals in that domain. For human hearing applications, enhancing speech intelli-gibility and speech quality is more critical than enhancing spectral profiles, which are important to machine hearing applications. we propose a joint processing which de-reverberates and de-noises in the temporal modulation domain. Consequently, we analyze the performance of each method by comparing the scores of speech intelli-gibility and quality using two speech corpora.
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