Spatial Mixup: Directional Loudness Modification as Data Augmentation for Sound Event Localization and Detection

Ricardo Falcon Perez, Kazuki Shimada, Yuichiro Koyama, Shusuke Takahashi, Yuki Mitsufuji

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

Data augmentation methods have shown great importance in diverse supervised learning problems where labeled data is scarce or costly to obtain. For sound event localization and detection (SELD) tasks several augmentation methods have been proposed, with most borrowing ideas from other domains such as images, speech, or monophonic audio. However, only a few exploit the spatial properties of a full 3D audio scene. We propose Spatial Mixup, as an application of parametric spatial audio effects for data augmentation, which modifies the directional properties of a multi-channel spatial audio signal encoded in the ambisonics domain. Similarly to beamforming, these modifications enhance or suppress signals arriving from certain directions, although the effect is less pronounced. Therefore enabling deep learning models to achieve invariance to small spatial perturbations. The method is evaluated with experiments in the DCASE 2021 Task 3 dataset, where spatial mixup increases performance over a non-augmented baseline, and compares to other well known augmentation methods. Furthermore, combining spatial mixup with other methods greatly improves performance.
Original languageEnglish
Title of host publicationProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
Place of PublicationUnited States
PublisherIEEE
Pages431-435
Number of pages5
ISBN (Electronic)978-1-6654-0540-9
ISBN (Print)978-1-6654-0541-6
DOIs
Publication statusPublished - 27 Apr 2022
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Acoustics, Speech, and Signal Processing - Singapore, Singapore
Duration: 23 May 202227 May 2022

Publication series

NameIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (Print)1520-6149
ISSN (Electronic)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
Country/TerritorySingapore
CitySingapore
Period23/05/202227/05/2022

Keywords

  • sound event detection
  • Spatial Audio
  • Sound localization

Fingerprint

Dive into the research topics of 'Spatial Mixup: Directional Loudness Modification as Data Augmentation for Sound Event Localization and Detection'. Together they form a unique fingerprint.

Cite this