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FilterAugment: An Acoustic Environmental Data Augmentation Method
[article]
2022
arXiv
pre-print
Acoustic environments affect acoustic characteristics of sound to be recognized by physically interacting with sound wave propagation. Thus, training acoustic models for audio and speech tasks requires regularization on various acoustic environments in order to achieve robust performance in real life applications. We propose FilterAugment, a data augmentation method for regularization of acoustic models on various acoustic environments. FilterAugment mimics acoustic filters by applying
arXiv:2110.03282v4
fatcat:wlwkz22sjrfpbpgc67w7pgqodm