  A mixed sample data augmentation strategy is proposed to enhance the
performance of models on audio scene classification, sound event
classification, and speech enhancement tasks. While there have been several
augmentation methods shown to be effective in improving image classification
performance, their efficacy toward time-frequency domain features of audio is
not assured. We propose a novel audio data augmentation approach named
"Specmix" specifically designed for dealing with time-frequency domain
features. The augmentation method consists of mixing two different data samples
by applying time-frequency masks effective in preserving the spectral
correlation of each audio sample. Our experiments on acoustic scene
classification, sound event classification, and speech enhancement tasks show
that the proposed Specmix improves the performance of various neural network
architectures by a maximum of 2.7%.
