# SSM-Net: feature learning for Music Structure Analysis using a Self-Similarity-Matrix based loss

arXiv: https://arxiv.org/abs/2211.08141 · HTML: https://mlsanity.com/paper/2211.08141/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 17 references that are in the map.
- First posted on arXiv 15 Nov 2022.

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

- [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://mlsanity.com/paper/1503.03832/) (2015)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Self-Normalizing Neural Networks](https://mlsanity.com/paper/1706.02515/) (2017)
- [madmom: a new Python Audio and Music Signal Processing Library](https://mlsanity.com/paper/1605.07008/) (2016)
- [Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization](https://mlsanity.com/paper/2101.11075/) (2021)
- [Unsupervised Learning of Deep Features for Music Segmentation](https://mlsanity.com/paper/2108.12955/) (2021)
- [Supervised Metric Learning for Music Structure Features](https://mlsanity.com/paper/2110.09000/) (2021)

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

- [Self-Similarity-Based and Novelty-based loss for music structure analysis](https://mlsanity.com/paper/2309.02243/) (2023)
- [Do Foundational Audio Encoders Understand Music Structure?](https://mlsanity.com/paper/2512.17209/) (2025)

## Abstract (arXiv)

In this paper, we propose a new paradigm to learn audio features for Music Structure Analysis (MSA). We train a deep encoder to learn features such that the Self-Similarity-Matrix (SSM) resulting from those approximates a ground-truth SSM. This is done by minimizing a loss between both SSMs. Since this loss is differentiable w.r.t. its input features we can train the encoder in a straightforward way. We successfully demonstrate the use of this training paradigm using the Area Under the Curve ROC (AUC) on the RWC-Pop dataset.