  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.
