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Data-Driven Harmonic Filters for Audio Representation Learning | IEEE Conference Publication | IEEE Xplore

Data-Driven Harmonic Filters for Audio Representation Learning


Abstract:

We introduce a trainable front-end module for audio representation learning that exploits the inherent harmonic structure of audio signals. The proposed architecture, com...Show More

Abstract:

We introduce a trainable front-end module for audio representation learning that exploits the inherent harmonic structure of audio signals. The proposed architecture, composed of a set of filters, compels the subsequent network to capture harmonic relations while preserving spectro-temporal locality. Since the harmonic structure is known to have a key role in human auditory perception, one can expect these harmonic filters to yield more efficient audio representations. Experimental results show that a simple convolutional neural network back-end with the proposed front-end outperforms state-of-the-art baseline methods in automatic music tagging, keyword spotting, and sound event tagging tasks.
Date of Conference: 04-08 May 2020
Date Added to IEEE Xplore: 09 April 2020
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Conference Location: Barcelona, Spain

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