# Deep Music Analogy Via Latent Representation Disentanglement

arXiv: https://arxiv.org/abs/1906.03626 · HTML: https://mlsanity.com/paper/1906.03626/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2019.

## Built on by (most cited first, 30 of 31)

- [Foundation Models for Music: A Survey](https://mlsanity.com/paper/2408.14340/) (2024)
- [MuPT: A Generative Symbolic Music Pretrained Transformer](https://mlsanity.com/paper/2404.06393/) (2024)
- [Controllable deep melody generation via hierarchical music structure representation](https://mlsanity.com/paper/2109.00663/) (2021)
- [Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models](https://mlsanity.com/paper/2405.09901/) (2024)
- [Retrieval Based Time Series Forecasting](https://mlsanity.com/paper/2209.13525/) (2022)
- [Loop Copilot: Conducting AI Ensembles for Music Generation and Iterative Editing](https://mlsanity.com/paper/2310.12404/) (2023)
- [Symbolic music generation conditioned on continuous-valued emotions](https://mlsanity.com/paper/2203.16165/) (2022)
- [Controlling Perceived Emotion in Symbolic Music Generation with Monte Carlo Tree Search](https://mlsanity.com/paper/2208.05162/) (2022)
- [Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling](https://mlsanity.com/paper/2310.16334/) (2023)
- [Exploring XAI for the Arts: Explaining Latent Space in Generative Music](https://mlsanity.com/paper/2308.05496/) (2023)
- [Is Disentanglement enough? On Latent Representations for Controllable Music Generation](https://mlsanity.com/paper/2108.01450/) (2021)
- [Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning](https://mlsanity.com/paper/2112.15110/) (2021)
- [Learning long-term music representations via hierarchical contextual constraints](https://mlsanity.com/paper/2202.06180/) (2022)
- [An Efficient Subpopulation-based Membership Inference Attack](https://mlsanity.com/paper/2203.02080/) (2022)
- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [Personalized Popular Music Generation Using Imitation and Structure](https://mlsanity.com/paper/2105.04709/) (2021)
- [BEAT: Tokenizing and Generating Symbolic Music by Uniform Temporal Steps](https://mlsanity.com/paper/2604.19532/) (2026)
- [MusER: Musical Element-Based Regularization for Generating Symbolic Music with Emotion](https://mlsanity.com/paper/2312.10307/) (2023)
- [Unsupervised Disentanglement of Content and Style via Variance-Invariance Constraints](https://mlsanity.com/paper/2407.03824/) (2024)
- [M2M-Gen: A Multimodal Framework for Automated Background Music Generation in Japanese Manga Using Large Language Models](https://mlsanity.com/paper/2410.09928/) (2024)
- [PhraseVAE and PhraseLDM: Latent Diffusion for Full-Song Multitrack Symbolic Music Generation](https://mlsanity.com/paper/2512.11348/) (2025)
- [Smoothness and effective regularizations in learned embeddings for shape matching](https://mlsanity.com/paper/2112.07289/) (2021)
- [Motif-Centric Representation Learning for Symbolic Music](https://mlsanity.com/paper/2309.10597/) (2023)
- [Generating High-quality Symbolic Music Using Fine-grained Discriminators](https://mlsanity.com/paper/2408.01696/) (2024)
- [From Generality to Mastery: Composer-Style Symbolic Music Generation via Large-Scale Pre-training](https://mlsanity.com/paper/2506.17497/) (2025)
- [ViTex: Visual Texture Control for Multi-Track Symbolic Music Generation via Discrete Diffusion Models](https://mlsanity.com/paper/2603.01984/) (2026)
- [Can Machines Generate Personalized Music? A Hybrid Favorite-aware Method for User Preference Music Transfer](https://mlsanity.com/paper/2201.08526/) (2022)
- [Expressive Music Data Processing and Generation](https://mlsanity.com/paper/2503.11896/) (2025)
- [Disentangling Score Content and Performance Style for Joint Piano Rendering and Transcription](https://mlsanity.com/paper/2509.23878/) (2025)
- [AnchorSteer: Self-Discovered Concept Injection for Structure-Preserving Music Editing](https://mlsanity.com/paper/2605.31053/) (2026)

## Abstract (arXiv)

Analogy-making is a key method for computer algorithms to generate both natural and creative music pieces. In general, an analogy is made by partially transferring the music abstractions, i.e., high-level representations and their relationships, from one piece to another; however, this procedure requires disentangling music representations, which usually takes little effort for musicians but is non-trivial for computers. Three sub-problems arise: extracting latent representations from the observation, disentangling the representations so that each part has a unique semantic interpretation, and mapping the latent representations back to actual music. In this paper, we contribute an explicitly-constrained variational autoencoder (EC2-VAE) as a unified solution to all three sub-problems. We focus on disentangling the pitch and rhythm representations of 8-beat music clips conditioned on chords. In producing music analogies, this model helps us to realize the imaginary situation of "what if" a piece is composed using a different pitch contour, rhythm pattern, or chord progression by borrowing the representations from other pieces. Finally, we validate the proposed disentanglement method using objective measurements and evaluate the analogy examples by a subjective study.