# Disentangled Multidimensional Metric Learning for Music Similarity

arXiv: https://arxiv.org/abs/2008.03720 · HTML: https://mlsanity.com/paper/2008.03720/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [CDPAM: Contrastive learning for perceptual audio similarity](https://mlsanity.com/paper/2102.05109/) (2021)
- [Contrastive Audio-Language Learning for Music](https://mlsanity.com/paper/2208.12208/) (2022)
- [MuseChat: A Conversational Music Recommendation System for Videos](https://mlsanity.com/paper/2310.06282/) (2023)
- [Language-Guided Music Recommendation for Video via Prompt Analogies](https://mlsanity.com/paper/2306.09327/) (2023)
- [Explainability in Music Recommender Systems](https://mlsanity.com/paper/2201.10528/) (2022)
- [Exploring Musical Roots: Applying Audio Embeddings to Empower Influence Attribution for a Generative Music Model](https://mlsanity.com/paper/2401.14542/) (2024)
- [Investigating Personalization Methods in Text to Music Generation](https://mlsanity.com/paper/2309.11140/) (2023)
- [Towards Proper Contrastive Self-supervised Learning Strategies For Music Audio Representation](https://mlsanity.com/paper/2207.04471/) (2022)
- [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)
- [And what if two musical versions don't share melody, harmony, rhythm, or lyrics ?](https://mlsanity.com/paper/2210.01256/) (2022)
- [GD-Retriever: Controllable Generative Text-Music Retrieval with Diffusion Models](https://mlsanity.com/paper/2506.17886/) (2025)
- [Artist Similarity with Graph Neural Networks](https://mlsanity.com/paper/2107.14541/) (2021)
- [Musical Audio Similarity with Self-supervised Convolutional Neural Networks](https://mlsanity.com/paper/2202.02112/) (2022)
- [Similar but Faster: Manipulation of Tempo in Music Audio Embeddings for Tempo Prediction and Search](https://mlsanity.com/paper/2401.08902/) (2024)
- [Lyrics Matter: Exploiting the Power of Learnt Representations for Music Popularity Prediction](https://mlsanity.com/paper/2512.05508/) (2025)
- [What Makes a Good Layer? Assessing the Layer-Wise Intrinsic Properties of Music Foundation Models](https://mlsanity.com/paper/2608.14819/) (2026)

## Abstract (arXiv)

Music similarity search is useful for a variety of creative tasks such as replacing one music recording with another recording with a similar "feel", a common task in video editing. For this task, it is typically necessary to define a similarity metric to compare one recording to another. Music similarity, however, is hard to define and depends on multiple simultaneous notions of similarity (i.e. genre, mood, instrument, tempo). While prior work ignore this issue, we embrace this idea and introduce the concept of multidimensional similarity and unify both global and specialized similarity metrics into a single, semantically disentangled multidimensional similarity metric. To do so, we adapt a variant of deep metric learning called conditional similarity networks to the audio domain and extend it using track-based information to control the specificity of our model. We evaluate our method and show that our single, multidimensional model outperforms both specialized similarity spaces and alternative baselines. We also run a user-study and show that our approach is favored by human annotators as well.