Animal Sound Separation using Dual-Path RNN and Classifier Loss

Yongmin Kim, Chulwon Choi, Yuanming Li, Hanseok Ko

Research output: Contribution to journalConference articlepeer-review

Abstract

Source separation is a task that aims to separate multiple sounds from mixed audio. Recent source separation studies suggest that they process the separation primarily through a time-domain approach and that most are carried out mainly on speech separation in a clean environment. A few studies of animal sound separation have made advances that would contain various environmental noises. To address this issue, we propose a novel method to separate the animal sounds among background noise and two overlapping sources. The proposed method focuses on taking into account the real-world environment by adding background noise. The proposed model structure adds a classification network to the dual-path recurrent neural network (DPRNN). In particular, the mixed audio becomes separated through a mask of the single source estimated within the DPRNN. The separated source is then converted into a mel-spectrogram for feature representation. We use the resulting feature as input to a classification network for classification for verification of the separation performance. The experimental results confirm that the proposed method achieves better separation performance than when using DPRNN alone.

Original languageEnglish
JournalProceedings of the International Congress on Acoustics
Publication statusPublished - 2022
Event24th International Congress on Acoustics, ICA 2022 - Gyeongju, Korea, Republic of
Duration: 2022 Oct 242022 Oct 28

Bibliographical note

Funding Information:
This work was supported by Korea Environment Industry & Technology Institute(KEITI) through Exotic Invasive Species Management Program, funded by Korea Ministry of Environment(MOE)(2021002280004)

Publisher Copyright:
© ICA 2022.All rights reserved

Keywords

  • Animal sound separation
  • deep learning
  • time domain

ASJC Scopus subject areas

  • Mechanical Engineering
  • Acoustics and Ultrasonics

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