# Metric Learning vs Classification for Disentangled Music Representation Learning

arXiv: https://arxiv.org/abs/2008.03729 · HTML: https://mlsanity.com/paper/2008.03729/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 9 of 9)

- [It's Time for Artistic Correspondence in Music and Video](https://mlsanity.com/paper/2206.07148/) (2022)
- [Differentiable Signal Processing With Black-Box Audio Effects](https://mlsanity.com/paper/2105.04752/) (2021)
- [Long-range Multimodal Pretraining for Movie Understanding](https://mlsanity.com/paper/2308.09775/) (2023)
- [On the Effect of Data-Augmentation on Local Embedding Properties in the Contrastive Learning of Music Audio Representations](https://mlsanity.com/paper/2401.08889/) (2024)
- [Artist Similarity with Graph Neural Networks](https://mlsanity.com/paper/2107.14541/) (2021)
- [Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search](https://mlsanity.com/paper/2401.08902/) (2024)
- [Self-Supervised Beat Tracking in Musical Signals with Polyphonic Contrastive Learning](https://mlsanity.com/paper/2201.01771/) (2022)
- [Representation Learning for the Automatic Indexing of Sound Effects Libraries](https://mlsanity.com/paper/2208.09096/) (2022)
- [Self-supervised Auxiliary Loss for Metric Learning in Music Similarity-based Retrieval and Auto-tagging](https://mlsanity.com/paper/2304.07449/) (2023)

## Abstract (arXiv)

Deep representation learning offers a powerful paradigm for mapping input data onto an organized embedding space and is useful for many music information retrieval tasks. Two central methods for representation learning include deep metric learning and classification, both having the same goal of learning a representation that can generalize well across tasks. Along with generalization, the emerging concept of disentangled representations is also of great interest, where multiple semantic concepts (e.g., genre, mood, instrumentation) are learned jointly but remain separable in the learned representation space. In this paper we present a single representation learning framework that elucidates the relationship between metric learning, classification, and disentanglement in a holistic manner. For this, we (1) outline past work on the relationship between metric learning and classification, (2) extend this relationship to multi-label data by exploring three different learning approaches and their disentangled versions, and (3) evaluate all models on four tasks (training time, similarity retrieval, auto-tagging, and triplet prediction). We find that classification-based models are generally advantageous for training time, similarity retrieval, and auto-tagging, while deep metric learning exhibits better performance for triplet-prediction. Finally, we show that our proposed approach yields state-of-the-art results for music auto-tagging.