# Deep Learning Techniques for Music Generation -- A Survey

arXiv: https://arxiv.org/abs/1709.01620 · HTML: https://mlsanity.com/paper/1709.01620/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Sep 2017.

## Built on by (most cited first, 30 of 49)

- [DiffRhythm: Blazingly Fast and Embarrassingly Simple End-to-End Full-Length Song Generation with Latent Diffusion](https://mlsanity.com/paper/2503.01183/) (2025)
- [Learning to Generate Music With Sentiment](https://mlsanity.com/paper/2103.06125/) (2021)
- [Multitrack Music Transformer](https://mlsanity.com/paper/2207.06983/) (2022)
- [Vision+X: A Survey on Multimodal Learning in the Light of Data](https://mlsanity.com/paper/2210.02884/) (2022)
- [FLUX that Plays Music](https://mlsanity.com/paper/2409.00587/) (2024)
- [The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding](https://mlsanity.com/paper/2110.10221/) (2021)
- [Structure-Enhanced Pop Music Generation via Harmony-Aware Learning](https://mlsanity.com/paper/2109.06441/) (2021)
- [Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling](https://mlsanity.com/paper/2504.15071/) (2025)
- [Exploring XAI for the Arts: Explaining Latent Space in Generative Music](https://mlsanity.com/paper/2308.05496/) (2023)
- [Motifs, Phrases, and Beyond: The Modelling of Structure in Symbolic Music Generation](https://mlsanity.com/paper/2403.07995/) (2024)
- [Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning](https://mlsanity.com/paper/2112.15110/) (2021)
- [Towards Automatic Instrumentation by Learning to Separate Parts in Symbolic Multitrack Music](https://mlsanity.com/paper/2107.05916/) (2021)
- [A Survey of Deep Learning Audio Generation Methods](https://mlsanity.com/paper/2406.00146/) (2024)
- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [A Comprehensive Survey on Generative AI for Video-to-Music Generation](https://mlsanity.com/paper/2502.12489/) (2025)
- [Evaluating Human-AI Interaction via Usability, User Experience and Acceptance Measures for MMM-C: A Creative AI System for Music Composition](https://mlsanity.com/paper/2504.14071/) (2025)
- [LoopNet: Musical Loop Synthesis Conditioned On Intuitive Musical Parameters](https://mlsanity.com/paper/2105.10371/) (2021)
- [Dual Learning Music Composition and Dance Choreography](https://mlsanity.com/paper/2201.11999/) (2022)
- [Challenges in creative generative models for music: a divergence maximization perspective](https://mlsanity.com/paper/2211.08856/) (2022)
- [Diff-V2M: A Hierarchical Conditional Diffusion Model with Explicit Rhythmic Modeling for Video-to-Music Generation](https://mlsanity.com/paper/2511.09090/) (2025)
- [Imaginative Walks: Generative Random Walk Deviation Loss for Improved Unseen Learning Representation](https://mlsanity.com/paper/2104.09757/) (2021)
- [Generating Coherent Drum Accompaniment With Fills And Improvisations](https://mlsanity.com/paper/2209.00291/) (2022)
- [An Comparative Analysis of Different Pitch and Metrical Grid Encoding Methods in the Task of Sequential Music Generation](https://mlsanity.com/paper/2301.13383/) (2023)
- [Controllable Lyrics-to-Melody Generation](https://mlsanity.com/paper/2306.02613/) (2023)
- [Text Conditioned Symbolic Drumbeat Generation using Latent Diffusion Models](https://mlsanity.com/paper/2408.02711/) (2024)
- [Applications and Advances of Artificial Intelligence in Music Generation:A Review](https://mlsanity.com/paper/2409.03715/) (2024)
- [Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming](https://mlsanity.com/paper/2210.01515/) (2022)
- [Transfer Learning for Underrepresented Music Generation](https://mlsanity.com/paper/2306.00281/) (2023)
- [AI (r)evolution -- where are we heading? Thoughts about the future of music and sound technologies in the era of deep learning](https://mlsanity.com/paper/2310.18320/) (2023)
- [Exploring Latent Spaces of Tonal Music using Variational Autoencoders](https://mlsanity.com/paper/2311.03621/) (2023)

## Abstract (arXiv)

This paper is a survey and an analysis of different ways of using deep learning (deep artificial neural networks) to generate musical content. We propose a methodology based on five dimensions for our analysis: Objective - What musical content is to be generated? Examples are: melody, polyphony, accompaniment or counterpoint. - For what destination and for what use? To be performed by a human(s) (in the case of a musical score), or by a machine (in the case of an audio file). Representation - What are the concepts to be manipulated? Examples are: waveform, spectrogram, note, chord, meter and beat. - What format is to be used? Examples are: MIDI, piano roll or text. - How will the representation be encoded? Examples are: scalar, one-hot or many-hot. Architecture - What type(s) of deep neural network is (are) to be used? Examples are: feedforward network, recurrent network, autoencoder or generative adversarial networks. Challenge - What are the limitations and open challenges? Examples are: variability, interactivity and creativity. Strategy - How do we model and control the process of generation? Examples are: single-step feedforward, iterative feedforward, sampling or input manipulation. For each dimension, we conduct a comparative analysis of various models and techniques and we propose some tentative multidimensional typology. This typology is bottom-up, based on the analysis of many existing deep-learning based systems for music generation selected from the relevant literature. These systems are described and are used to exemplify the various choices of objective, representation, architecture, challenge and strategy. The last section includes some discussion and some prospects.