Sampling-Frequency-Independent Audio Source Separation Using Convolution Layer Based on Impulse Invariant Method

Koichi Saito, Tomohiko Nakamura, Kohei Yatabe, Yuma Koizumi*, Hiroshi Saruwatari

*この研究の対応する著者

研究成果: Conference contribution

抄録

Audio source separation is often used as preprocessing of various applications, and one of its ultimate goals is to construct a single versatile model capable of dealing with the varieties of audio signals. Since sampling frequency, one of the audio signal varieties, is usually application specific, the preceding audio source separation model should be able to deal with audio signals of all sampling frequencies specified in the target applications. However, conventional models based on deep neural networks (DNNs) are trained only at the sampling frequency specified by the training data, and there are no guarantees that they work with unseen sampling frequencies. In this paper, we propose a convolution layer capable of handling arbitrary sampling frequencies by a single DNN. Through music source separation experiments, we show that the introduction of the proposed layer enables a conventional audio source separation model to consistently work with even unseen sampling frequencies.

本文言語English
ホスト出版物のタイトル29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
出版社European Signal Processing Conference, EUSIPCO
ページ321-325
ページ数5
ISBN(電子版)9789082797060
DOI
出版ステータスPublished - 2021
イベント29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
継続期間: 2021 8月 232021 8月 27

出版物シリーズ

名前European Signal Processing Conference
2021-August
ISSN(印刷版)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
国/地域Ireland
CityDublin
Period21/8/2321/8/27

ASJC Scopus subject areas

  • 信号処理
  • 電子工学および電気工学

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