AN INVESTIGATION OF THE EFFECTIVENESS OF PHASE FOR AUDIO CLASSIFICATION

研究成果: 書籍/レポート タイプへの寄稿会議への寄与

抄録

While log-amplitude mel-spectrogram has widely been used as the feature representation for processing speech based on deep learning, the effectiveness of another aspect of speech spectrum, i.e., phase information, was shown recently for tasks such as speech enhancement and source separation. In this study, we extensively investigated the effectiveness of including phase information of signals for eight audio classification tasks. We constructed a learnable front-end that can compute the phase and its derivatives based on a time-frequency representation with mel-like frequency axis. As a result, experimental results showed significant performance improvement for musical pitch detection, musical instrument detection, language identification, speaker identification, and birdsong detection. On the other hand, overfitting to the recording condition was observed for some tasks when the instantaneous frequency was used. The results implied that the relationship between the phase values of adjacent elements is more important than the phase itself in audio classification.

本文言語英語
ホスト出版物のタイトル2022 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
ページ3708-3712
ページ数5
ISBN(電子版)9781665405409
DOI
出版ステータス出版済み - 2022
イベント47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022 - Virtual, Online, シンガポール
継続期間: 5月 23 20225月 27 2022

出版物シリーズ

名前ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
2022-May
ISSN(印刷版)1520-6149

会議

会議47th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2022
国/地域シンガポール
CityVirtual, Online
Period5/23/225/27/22

!!!All Science Journal Classification (ASJC) codes

  • ソフトウェア
  • 信号処理
  • 電子工学および電気工学

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