Mixing-Specific Data Augmentation Techniques for Improved Blind Violin/Piano Source Separation

Ching Yu Chiu, Wen Yi Hsiao, Yin Cheng Yeh, Yi Hsuan Yang, Alvin Wen Yu Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Blind music source separation has been a popular and active subject of research in both the music information retrieval and signal processing communities. To counter the lack of available multi-track data for supervised model training, a data augmentation method that creates artificial mixtures by combining tracks from different songs has been shown useful in recent works. Following this light, we examine further in this paper extended data augmentation methods that consider more sophisticated mixing settings employed in the modern music production routine, the relationship between the tracks to be combined, and factors of silence. As a case study, we consider the separation of violin and piano tracks in a violin piano ensemble, evaluating the performance in terms of common metrics, namely SDR, SIR, and SAR. In addition to examining the effectiveness of these new data augmentation methods, we also study the influence of the amount of training data. Our evaluation shows that the proposed mixing-specific data augmentation methods can help improve the performance of a deep learning-based model for source separation, especially in the case of small training data.

Original languageEnglish
Title of host publicationIEEE 22nd International Workshop on Multimedia Signal Processing, MMSP 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728193205
DOIs
Publication statusPublished - 2020 Sep 21
Event22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020 - Virtual, Tampere, Finland
Duration: 2020 Sep 212020 Sep 24

Publication series

NameIEEE 22nd International Workshop on Multimedia Signal Processing, MMSP 2020

Conference

Conference22nd IEEE International Workshop on Multimedia Signal Processing, MMSP 2020
CountryFinland
CityVirtual, Tampere
Period20-09-2120-09-24

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Media Technology

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