Sample Mixed-Based Data Augmentation for Domestic Audio Tagging

TL;DR

This paper introduces a novel sample mixed data augmentation approach for domestic audio tagging, significantly improving model performance and achieving state-of-the-art results on the DCASE 2016 dataset.

Contribution

It explores and applies mixup, SamplePairing, and extrapolation data augmentation methods to enhance deep learning models for audio tagging.

Findings

Mixup approach achieves EER of 0.10 on DCASE 2016 dataset.

Data augmentation outperforms baseline without augmentation.

Sample mixed augmentation improves generalization in audio tagging.

Abstract

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding background noise do not show much improvement in audio tagging. In this paper, we explore the sample mixed data augmentation for the domestic audio tagging task, including mixup, SamplePairing and extrapolation. We apply a convolutional recurrent neural network (CRNN) with attention module with log-scaled mel spectrum as a baseline system. In our experiments, we achieve an state-of-the-art of equal error rate (EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming the baseline system without data augmentation.