Filteraugment: An acoustic environmental data augmentation method

Hyeonuk nam, Seong-hu Kim, Yong-Hwa Park

Abstract:

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 Filter-
Augment, 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.