| Summary: | 碩士 === 國立交通大學 === 電信工程研究所 === 106 === Revealed by psychophysical and neuro-physiological studies, the cochlea analyzes the incoming sound in the time and logarithmic-frequency domains. Afterward, the neural activities pass through the auditory pathway to the primary auditory cortex (A1) for further analysis. From the functional point of view, the cochlea produces a 2-D auditory spectrogram and the A1 analyzes the 2-D spectrogram. In this thesis, we propose a neural network (NN) to simulate an attentional auditory model and apply it to speaker identification. The proposed NN consists of 1-D and 2-D convolutional neural networks which mimic the functions of the cochlea and the cortex respectively. By deriving initial kernels of the convolutional layers from the neuro-physiological auditory model, we demonstrated that the proposed NN can quickly reach the convergence state with high performance. In addition, even without training, the proposed system with auditory model based kernels outperforms the randomly initialized NN in speaker identification.
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