# Musical Audio Similarity with Self-supervised Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2202.02112 · HTML: https://mlsanity.com/paper/2202.02112/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 16 references that are in the map.
- First posted on arXiv 4 Feb 2022.

## Builds on (most cited first, 7 of 7)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Sampling Matters in Deep Embedding Learning](https://mlsanity.com/paper/1706.07567/) (2017)
- [Contrastive Learning of General-Purpose Audio Representations](https://mlsanity.com/paper/2010.10915/) (2020)
- [Contrastive Learning of Musical Representations](https://mlsanity.com/paper/2103.09410/) (2021)
- [Unsupervised Learning of Semantic Audio Representations](https://mlsanity.com/paper/1711.02209/) (2017)
- [Disentangled Multidimensional Metric Learning for Music Similarity](https://mlsanity.com/paper/2008.03720/) (2020)

## Built on by (most cited first, 2 of 2)

- [CrossMuSim: A Cross-Modal Framework for Music Similarity Retrieval with LLM-Powered Text Description Sourcing and Mining](https://mlsanity.com/paper/2503.23128/) (2025)
- [Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging](https://mlsanity.com/paper/2304.07449/) (2023)

## Abstract (arXiv)

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.