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

Source: [SpecMix : A Mixed Sample Data Augmentation method for Training withTime-Frequency Domain Features](<https://www.sota2.com/research/paper/68fa6a7f1e209131cea28a7a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:49.196Z

Authors: Gwantae Kim, David K. Han, Hanseok Ko

Published: 2021-08-06T09:37:27.000Z

## Abstract

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%.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2108.03020>)

[PDF](<https://arxiv.org/pdf/2108.03020.pdf>)