Effectiveness of Inter- and Intra-Subarray Spatial Features for Acoustic Scene Classification

Takao Kawamura; Yuma Kinoshita; Nobutaka Ono; Robin Scheibler

ICASSP 2023accepted0 citations

Effectiveness of Inter- and Intra-Subarray Spatial Features for Acoustic Scene Classification

Takao Kawamura, Yuma Kinoshita, Nobutaka Ono, Robin Scheibler

Abstract

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.

BibTeX

@inproceedings{icassp2023_effectivenessofi,
  title = {Effectiveness of Inter- and Intra-Subarray Spatial Features for Acoustic Scene Classification},
  author = {Takao Kawamura and Yuma Kinoshita and Nobutaka Ono and Robin Scheibler},
  booktitle = {ICASSP 2023},
  year = {2023}
}