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arXiv · 2202.02112

Musical Audio Similarity with Self-supervised Convolutional Neural Networks

Carl Thomé·Sebastian Piwell·Oscar Utterbäck
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Abstract

We have built a music similarity search engine that lets video producers search by listenable music excerpts, as a complement to traditional full-text search. Our system suggests similar sounding track segments in a large music catalog by training a self-supervised convolutional neural network with triplet loss terms and musical transformations. Semi-structured user interviews demonstrate that we can successfully impress professional video producers with the quality of the search experience, and perceived similarities to query tracks averaged 7.8/10 in user testing. We believe this search tool will make for a more natural search experience that is easier to find music to soundtrack videos with.

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BibTeXRIS

Carl Thomé, Sebastian Piwell, Oscar Utterbäck. 2022-02-04. Musical Audio Similarity with Self-supervised Convolutional Neural Networks. https://arxiv.org/abs/2202.02112

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