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%.

本文提出了一种混合样本数据增强策略 Specmix，可以提高模型在音频场景分类、声音事件分类和语音增强任务中的性能。通过应用时间频率掩码来混合两个不同的数据样本，有效地保留音频数据中的频谱相关性，实验表明，该方法可以最大程度地提高各种神经网络架构的性能，提高了 2.7%。