DNN-Based Near- and Far-Field Source Separation Using Spherical-Harmonic-Analysis-Based Acoustic Features

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説明

We propose the combination of a physical-model-based and a deep-learning (DL)-based source separation for near- and far-field source separation. The DL-based near- and far-field source separation method uses spherical-harmonic-analysis-based acoustic features. Deep learning is a state-of-the-art technique for source separation. In this approach, a deep neural network (D NN) is used to predict a time-frequency (T-F) mask. To accurately predict a T-F mask, it is necessary to use acoustic features that have high mutual information with the oracle T-F mask. However, the effective acoustic features to separate near- and far-field sources are unknown. In this study, low-frequency-band near- and far-field sources are estimated based on spherical harmonic analysis and used as acoustic features. Subsequently, a DNN predicts a T-F mask to separate all frequency bands. Our experimental results show that the proposed method improved the signal-to-distortion-rate by 6–8 dB compared to the harmonic-analysis-based method.

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