  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 different weights on
frequency bands, therefore enables model to extract relevant information from
wider frequency region. It is an improved version of frequency masking which
masks information on random frequency bands. FilterAugment improved sound event
detection (SED) model performance by 6.50% while frequency masking only
improved 2.13% in terms of polyphonic sound detection score (PSDS). It achieved
equal error rate (EER) of 1.22% when applied to a text-independent speaker
verification model, outperforming model used frequency masking with EER of
1.26%. Prototype of FilterAugment was applied in our participation in DCASE
2021 challenge task 4, and played a major role in achieving the 3rd rank.
