Abstract
Despite consistent advancement in powerful deep learning techniques in recent years, large amounts of training data are still necessary for the models to avoid overfitting. Synthetic datasets using generative adversarial networks (GAN) have recently been generated to overcome this problem. Nevertheless, despite advancements, GAN-based methods are usually hard to train or fail to generate high-quality data samples. In this paper, we propose an environmental sound classification (ESC) augmentation technique based on the diffusion probabilistic model (DPM) with DPM-Solver ++ for fast sampling. In addition, to ensure the quality of the generated spectrograms, we propose a top-k selection technique to filter out the low-quality synthetic data samples. According to the experiment results, the synthetic data samples have similar features to the original dataset and can significantly increase the classification accuracy of different state-of-the-art models compared with traditional data augmentation techniques. The public code is available on https://github.com/JNAIC/DPMs-for-Audio-Data-Augmentation.
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Chen, Y., Yan, Z., Zhu, Y., Ren, Z., Shen, J., Huang, Y. (2023). Data Augmentation for Environmental Sound Classification Using Diffusion Probabilistic Model with Top-K Selection Discriminator. In: Huang, DS., Premaratne, P., Jin, B., Qu, B., Jo, KH., Hussain, A. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2023. Lecture Notes in Computer Science, vol 14087. Springer, Singapore. https://doi.org/10.1007/978-981-99-4742-3_23
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