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A mixed sample data augmentation strategy is proposed to enhance theperformance of models on audio scene classification, sound eventclassification, and speech enhancement tasks. While there have been severalaugmentation methods shown to be effective in improving image classificationperformance, their efficacy toward time-frequency domain features of audio isnot assured. We propose a novel audio data augmentation approach named"Specmix" specifically designed for dealing with time-frequency domainfeatures. The augmentation method consists of mixing two different data samplesby applying time-frequency masks effective in preserving the spectralcorrelation of each audio sample. Our experiments on acoustic sceneclassification, sound event classification, and speech enhancement tasks showthat the proposed Specmix improves the performance of various neural networkarchitectures by a maximum of 2.7%.

SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features