{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/generating-music-using-an-lstm-network","title":"Generating Music using an LSTM Network","arxiv_id":"1804.07300","date":"2018-04-18","proceeding":null,"authors":["Nikhil Kotecha","Paul Young"],"abstract":"A model of music needs to have the ability to recall past details and have a\nclear, coherent understanding of musical structure. Detailed in the paper is a\nneural network architecture that predicts and generates polyphonic music\naligned with musical rules. The probabilistic model presented is a Bi-axial\nLSTM trained with a kernel reminiscent of a convolutional kernel. When analyzed\nquantitatively and qualitatively, this approach performs well in composing\npolyphonic music. Link to the code is provided.","url_abs":"http://arxiv.org/abs/1804.07300v1","url_pdf":"http://arxiv.org/pdf/1804.07300v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"generating-music-using-an-lstm-network","repo_url":"https://github.com/nikhil-kotecha/Generating_Music","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}