| Summary: | 碩士 === 國立交通大學 === 電機工程學系 === 106 === De-reverberation to cancel the reverberant effect and de-noise have always been important topics in speech signal processing. In this thesis, we first analyze the re-verberant effect through a series of approximations and simplifications and use deep learning techniques to apply mapping and masking methods for de-reverberation. By using the reference modulation magnitude derived from a different sentence as the input to the neural network during training, the neural network performs well on de-reverberation for unseen environments. Next, to handle the real-world problem, we propose a two-stage processing which de-reverberates in the modulation domain and de-noises in the spectrogram domain respectively. The artificial additive noise produced from the first de-reverberation stage will also be canceled in the second stage along with environmental additive noise. The reconstruction of speech can be improved by multiple-stage learning. For human hearing applications, speech intelli-gibility and speech quality are considered as important evaluation criteria. Conse-quently, we analyze the advantages and disadvantages of each network structure by comparing the scores of speech intelligibility and quality using two speech corpora.
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