Publications
カンファレンス (国際) Effectiveness of Inter- and Intra-subarray Spatial Features for Acoustic Scene Classification
Takao Kawamura (Tokyo Metropolitan University), Yuma Kinoshita (Tokyo Metropolitan University), Nobutaka Ono (Tokyo Metropolitan University), Robin Scheibler
2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2023)
2023.6.4
In this paper, we investigate the effectiveness of spatial features for acoustic scene classification (ASC) with distributed microphones. Assuming that multiple subarrays, each containing multiple micro-phones, are distributed and synchronized, we consider two types of generalized cross-correlation phase transform (GCC-PHAT) as spatial features: the intra- and inter-subarray GCC-PHATs. They are obtained from channels within the same subarray and between different subarrays, respectively. The log-Mel spectrogram as a spectral feature and the intra- or inter-subarray GCC-PHAT are processed in the neural network. The experimental results show that increasing the number of channels did not markedly improve the ASC performance when using the spectral features alone. However, using either of the GCC-PHATs as the spatial feature together with the spectral features successfully improved the ASC performance.
Paper :
Effectiveness of Inter- and Intra-subarray Spatial Features for Acoustic Scene Classification
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