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List of articles related to deep learning applied to music
| Date | Stars |
|---|---|
| 2026-07-24 | 2974 |
| 2026-07-25 | 2974 |
| 2026-07-28 | 2974 |
| 2026-07-30 | 2974 |
| 2026-07-31 | 2975 |
| 2026-08-06 | 2977 |
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⚠️ This repo is unmaintained. While the info are still relevant, contributions to keep it up to date are welcome! A good starting point are the articles referenced here: https://github.com/ybayle/awesome-deep-learning-music/issues/5 <img align="right" src="fig/logo.png"> # Deep Learning for Music (DL4M) [](https://github.com/sindresorhus/awesome) By [Yann Bayle](http://yannbayle.fr/english/index.php) ([Website](http://yannbayle.fr/english/index.php), [GitHub](https://github.com/ybayle)) from LaBRI ([Website](http://www.labri.fr/), [Twitter](https://twitter.com/labriOfficial/)), Univ. Bordeaux ([Website](https://www.u-bordeaux.fr/), [Twitter](https://twitter.com/univbordeaux)), CNRS ([Website](http://www.cnrs.fr/), [Twitter](https://twitter.com/CNRS)) and SCRIME ([Website](https://scrime.u-bordeaux.fr/)). **TL;DR** Non-exhaustive list of scientific articles on deep learning for music: [summary](#dl4m-summary) (Article title, pdf link and code), [details](dl4m.tsv) (table - more info), [details](dl4m.bib) (bib - all info) The role of this curated list is to gather scientific articles, thesis and reports that use deep learning approaches applied to music. The list is currently under construction but feel free to contribute to the missing fields and to add other resources! To do so, please refer to the [How To Contribute](#how-to-contribute) section. The resources provided here come from my review of the state-of-the-art for my PhD Thesis for which an article is being written. There are already surveys on deep learning for [music generation](https://arxiv.org/pdf/1709.01620.pdf), [speech separation](https://arxiv.org/ftp/arxiv/papers/1708/1708.07524.pdf) and [speaker identification](https://www.researchgate.net/profile/Seyed_Reza_Shahamiri/publication/319158024_Speaker_Identification_Features_Extraction_Methods_A_Systematic_Review/links/599e2816aca272dff12fdef1/Speaker-Identification-Features-Extraction-Methods-A-Systematic-Review.pdf). However, these surveys do not cover music information retrieval tasks that are included in this repository. ## Table of contents - [DL4M summary](#dl4m-summary) - [DL4M details](#dl4m-details) - [Code without articles](#code-without-articles) - [Statistics and visualisations](#statistics-and-visualisations) - [Advices for reviewers of dl4m articles](#advices-for-reviewers-of-dl4m-articles) - [How To Contribute](#how-to-contribute) - [FAQ](#faq) - [Acronyms used](#acronyms-used) - [Sources](#sources) - [Contributors](#contributors) - [Other useful related lists](#other-useful-related-lists-and-resources) - [Cited by](#cited-by) - [License](#license) ## DL4M summary | Year | Articles, Thesis and Reports | Code | |------|-------------------------------|------| | 1988 | Neural net modeling of music | No | | 1988 | [Creation by refinement: A creativity paradigm for gradient descent learning networks](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=23933) | No | | 1988 | A sequential network design for musical applications | No | | 1989 | [The representation of pitch in a neural net model of chord classification](http://www.jstor.org/stable/3679550) | No | | 1989 | [Algorithms for music composition by neural nets: Improved CBR paradigms](https://quod.lib.umich.edu/cgi/p/pod/dod-idx/algorithms-for-music-composition.pdf?c=icmc;idno=bbp2372.1989.044;format=pdf) | No | | 1989 | [A connectionist approach to algorithmic composition](http://www.jstor.org/stable/3679551) | No | | 1994 | [Neural network music composition by prediction: Exploring the benefits of psychoacoustic constraints and multi-scale processing](http://www-labs.iro.umontreal.ca/~pift6080/H09/documents/papers/mozer-music.pdf) | No | | 1995 | [Automatic source identification of monophonic musical instrument sounds](https://www.researchgate.net/publication/3622871_Automatic_source_identification_of_monophonic_musical_instr
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:11fa9798f772ef42, topic:awesome, topic:awesome-list, readme:curated list
matched fp:11fa9798f772ef42, topic:deep-learning, topic:neural-network
matched fp:11fa9798f772ef42, topic:audio-processing, readme:music generation
