# Music transcription modelling and composition using deep learning

arXiv: https://arxiv.org/abs/1604.08723 · HTML: https://mlsanity.com/paper/1604.08723/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2016.

## Built on by (most cited first, 30 of 36)

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- [Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation](https://mlsanity.com/paper/2412.07948/) (2024)
- [Variable-Length Music Score Infilling via XLNet and Musically Specialized Positional Encoding](https://mlsanity.com/paper/2108.05064/) (2021)
- [A Review of Intelligent Music Generation Systems](https://mlsanity.com/paper/2211.09124/) (2022)
- [GTR-CTRL: Instrument and Genre Conditioning for Guitar-Focused Music Generation with Transformers](https://mlsanity.com/paper/2302.05393/) (2023)
- [Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey](https://mlsanity.com/paper/2402.17467/) (2024)
- [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)
- [Emotion-driven Piano Music Generation via Two-stage Disentanglement and Functional Representation](https://mlsanity.com/paper/2407.20955/) (2024)
- [Melody Infilling with User-Provided Structural Context](https://mlsanity.com/paper/2210.02829/) (2022)
- [ProgGP: From GuitarPro Tablature Neural Generation To Progressive Metal Production](https://mlsanity.com/paper/2307.05328/) (2023)
- [Exploring Variational Auto-Encoder Architectures, Configurations, and Datasets for Generative Music Explainable AI](https://mlsanity.com/paper/2311.08336/) (2023)
- [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)
- [Exploring Sampling Techniques for Generating Melodies with a Transformer Language Model](https://mlsanity.com/paper/2308.09454/) (2023)
- [Between the AI and Me: Analysing Listeners' Perspectives on AI- and Human-Composed Progressive Metal Music](https://mlsanity.com/paper/2407.21615/) (2024)
- [Exploring Tokenization Methods for Multitrack Sheet Music Generation](https://mlsanity.com/paper/2410.17584/) (2024)
- [On the Typicality of Musical Sequences](https://mlsanity.com/paper/2211.13016/) (2022)
- [From Words to Music: A Study of Subword Tokenization Techniques in Symbolic Music Generation](https://mlsanity.com/paper/2304.08953/) (2023)
- [Six Dragons Fly Again: Reviving 15th-Century Korean Court Music with Transformers and Novel Encoding](https://mlsanity.com/paper/2408.01096/) (2024)
- [Symbotunes: unified hub for symbolic music generative models](https://mlsanity.com/paper/2410.20515/) (2024)
- [Towards Hallucination-Free Music: A Reinforcement Learning Preference Optimization Framework for Reliable Song Generation](https://mlsanity.com/paper/2508.05011/) (2025)
- [Estimating Musical Surprisal from Audio in Autoregressive Diffusion Model Noise Spaces](https://mlsanity.com/paper/2508.05306/) (2025)
- [Learning a Word-Level Language Model with Sentence-Level Noise Contrastive Estimation for Contextual Sentence Probability Estimation](https://mlsanity.com/paper/2103.07875/) (2021)
- [Differential Music: Automated Music Generation Using LSTM Networks with Representation Based on Melodic and Harmonic Intervals](https://mlsanity.com/paper/2108.10449/) (2021)
- [Actions Speak Louder than Listening: Evaluating Music Style Transfer based on Editing Experience](https://mlsanity.com/paper/2110.12855/) (2021)

## Abstract (arXiv)

We apply deep learning methods, specifically long short-term memory (LSTM) networks, to music transcription modelling and composition. We build and train LSTM networks using approximately 23,000 music transcriptions expressed with a high-level vocabulary (ABC notation), and use them to generate new transcriptions. Our practical aim is to create music transcription models useful in particular contexts of music composition. We present results from three perspectives: 1) at the population level, comparing descriptive statistics of the set of training transcriptions and generated transcriptions; 2) at the individual level, examining how a generated transcription reflects the conventions of a music practice in the training transcriptions (Celtic folk); 3) at the application level, using the system for idea generation in music composition. We make our datasets, software and sound examples open and available: urlhttps://github.com/IraKorshunova/folk-rnn.