Shuffling and Mixing Data Augmentation for Environmental Sound Classification

Abstract

Smart speakers have been recently adopted and widely used in consumer homes, largely as a communication interface between human and machines. In addition, these speakers can be used to monitor sounds other than human voice, for example, to watch over elderly people living alone, and to notify if there are changes in their usual activities that may affect their health. In this paper, we focus on the sound classification using machine learning, which usually requires a lot of training data to achieve good accuracy. Our main contribution is a data augmentation technique that generates new sound by shuffling and mixing two existing sounds of the same class in the dataset. This technique creates new variations on both the temporal sequence and the density of the sound events. We show in DCASE 2018 Task 5 that the proposed data augmentation method with our proposed convolutional neural network (CNN) achieves an average of macroaveraged F1 score of 89.95% over 4 folds of the development dataset. This is a significant improvement from the baseline result of 84.50%. In addition, we also verify that our proposed data augmentation technique can improve the classification performance on the Urban Sound 8K dataset.