ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

SpecMix : A Mixed Sample Data Augmentation Method for Training with Time-Frequency Domain Features

Gwantae Kim, David K. Han, Hanseok Ko

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%.


doi: 10.21437/Interspeech.2021-103

Cite as: Kim, G., Han, D.K., Ko, H. (2021) SpecMix : A Mixed Sample Data Augmentation Method for Training with Time-Frequency Domain Features. Proc. Interspeech 2021, 546-550, doi: 10.21437/Interspeech.2021-103

@inproceedings{kim21c_interspeech,
  author={Gwantae Kim and David K. Han and Hanseok Ko},
  title={{SpecMix : A Mixed Sample Data Augmentation Method for Training with Time-Frequency Domain Features}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={546--550},
  doi={10.21437/Interspeech.2021-103}
}