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Unsupervised Discriminative Learning of Sounds for Audio Event Classification
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Sascha Hornauer and Ke Li and Stella X. Yu and Shabnam Ghaffarzadegan and Liu Ren
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IEEE International Conference on Acoustics, Speech and Signal Processing, Online, 6-11 June 2021
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Paper
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Slides
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arXiv
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Abstract
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Recent progress in network-based audio event classification has shown the benefit of pre-training models on visual data such as ImageNet. While this process allows knowledge transfer across different domains, training a model on large-scale visual datasets is time consuming. On several audio event classification benchmarks, we show a fast and effective alternative that pre-trains the model unsupervised, only on audio data and yet delivers on-par performance with ImageNet pre-training. Furthermore, we show that our discriminative audio learning can be used to transfer knowledge across audio datasets and optionally include ImageNet pre-training.
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Keywords
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audio event classification, unsupervised representation learning
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