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

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussaConference contributionScientificvertaisarvioitu

Abstrakti

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.
AlkuperäiskieliEnglanti
OtsikkoProceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing
JulkaisupaikkaUnited States
KustantajaIEEE
Sivut431-435
Sivumäärä5
ISBN (elektroninen)978-1-6654-0540-9
ISBN (painettu)978-1-6654-0541-6
DOI - pysyväislinkit
TilaJulkaistu - 27 huhtik. 2022
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisuussa
TapahtumaIEEE International Conference on Acoustics, Speech, and Signal Processing - Singapore, Singapore
Kesto: 23 toukok. 202227 toukok. 2022

Julkaisusarja

NimiIEEE International Conference on Acoustics, Speech and Signal Processing
ISSN (painettu)1520-6149
ISSN (elektroninen)2379-190X

Conference

ConferenceIEEE International Conference on Acoustics, Speech, and Signal Processing
LyhennettäICASSP
Maa/AlueSingapore
KaupunkiSingapore
Ajanjakso23/05/202227/05/2022

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