A Comparison on Data Augmentation Methods Based on Deep Learning for Audio Classification
Shengyun Wei, Shun Zou, Feifan Liao, Weimin Lang
Abstract
Abstract Deep learning focuses on the representation of the input data and generalization of the model. It is well known that data augmentation can combat overfitting and improve the generalization ability of deep neural network. In this paper, we summarize and compare multiple data augmentation methods for audio classification. These strategies include traditional methods on raw audio signal, as well as the current popular augmentation of linear interpolation and nonlinear mixing on the spectrum. We explore the generation of new samples, the transformation of labels, and the combination patterns of samples and labels of each data augmentation method. Finally, inspired by SpecAugment and Mixup, we propose an effective and easy to implement data augmentation method, which we call Mixed frequency Masking data augmentation. This method adopts nonlinear combination method to construct new samples and linear method to construct labels. All methods are verified on the Freesound Dataset Kaggle2018 dataset, and ResNet is adopted as the classifier. The baseline system uses the log-mel spectrogram feature as the input. We use mean Average Precision @3 (mAP@3) as the evaluation metric to evaluate the performance of all data augmentation methods.