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Audio tagging has attracted increasing attention since last decade and hasvarious potential applications in many fields. The objective of audio taggingis to predict the labels of an audio clip. Recently deep learning methods havebeen applied to audio tagging and have achieved state-of-the-art performance,which provides a poor generalization ability on new data. However due to thelimited size of audio tagging data such as DCASE data, the trained models tendto result in overfitting of the network. Previous data augmentation methodssuch as pitch shifting, time stretching and adding background noise do not showmuch improvement in audio tagging. In this paper, we explore the sample mixeddata augmentation for the domestic audio tagging task, including mixup,SamplePairing and extrapolation. We apply a convolutional recurrent neuralnetwork (CRNN) with attention module with log-scaled mel spectrum as a baselinesystem. 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, outperformingthe baseline system without data augmentation.

Sample Mixed-Based Data Augmentation for Domestic Audio Tagging