{"url":"/task/music-modeling","name":"Music Modeling","slug":"music-modeling","description_markdown":"<span style=\"color:grey; opacity: 0.6\">( Image credit: [R-Transformer](https://arxiv.org/pdf/1907.05572v1.pdf) )</span>","categories":[{"name":"Music","url":"/area/music"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":34,"papers_with_code":24,"benchmarks":2,"benchmark_tables_in_archive":2,"benchmark_tables_shown":2,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":6,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/music-modeling-on-jsb-chorales","slug":"music-modeling-on-jsb-chorales","dataset":"JSB Chorales","dataset_url":"/dataset/jsb-chorales","rows_in_archive":10,"metrics":["NLL","Parameters"],"first_row_in_archive_order":{"model":"TonicNet","paper_title":"JS Fake Chorales: a Synthetic Dataset of Polyphonic Music with Human Annotation","paper_url":"/paper/js-fake-chorales-a-synthetic-dataset-of","paper_date":"2021-07-21","arxiv_id":"2107.10388","code_links":[{"title":"omarperacha/TonicNet","url":"https://github.com/omarperacha/TonicNet"},{"title":"omarperacha/js-fakes","url":"https://github.com/omarperacha/js-fakes"}],"syntology":null}},{"leaderboard":"/sota/music-modeling-on-nottingham","slug":"music-modeling-on-nottingham","dataset":"Nottingham","dataset_url":"/dataset/nottingham","rows_in_archive":8,"metrics":["NLL","Parameters"],"first_row_in_archive_order":{"model":"R-Transformer","paper_title":"R-Transformer: Recurrent Neural Network Enhanced Transformer","paper_url":"/paper/r-transformer-recurrent-neural-network","paper_date":"2019-07-12","arxiv_id":"1907.05572","code_links":[{"title":"DSE-MSU/R-transformer","url":"https://github.com/DSE-MSU/R-transformer"},{"title":"sfox14/butterfly-r-transformer","url":"https://github.com/sfox14/butterfly-r-transformer"}],"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":2}}}],"datasets":[{"url":"/dataset/lakh-midi-dataset","name":"Lakh MIDI Dataset","full_name":"","num_papers_in_archive":36},{"url":"/dataset/jsb-chorales","name":"JSB Chorales","full_name":"","num_papers_in_archive":33},{"url":"/dataset/music21","name":"Music21","full_name":"Music21","num_papers_in_archive":33},{"url":"/dataset/lakh-pianoroll-dataset","name":"Lakh Pianoroll Dataset","full_name":"","num_papers_in_archive":10},{"url":"/dataset/nottingham","name":"Nottingham","full_name":"Nottingham","num_papers_in_archive":4},{"url":"/dataset/js-fake-chorales","name":"JS Fake Chorales","full_name":"JS Fake Chorales","num_papers_in_archive":3}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":24,"of":24,"tagged_in_all":34,"items":[{"url":"/paper/an-empirical-evaluation-of-generic","title":"An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling","date":"2018-03-04","arxiv_id":"1803.01271","repositories_listed":35,"syntology":{"n":10,"n_ran":2,"n_unverified":8,"n_pointer_only":1}},{"url":"/paper/lstm-a-search-space-odyssey","title":"LSTM: A Search Space Odyssey","date":"2015-03-13","arxiv_id":"1503.04069","repositories_listed":17,"syntology":null},{"url":"/paper/empirical-evaluation-of-gated-recurrent","title":"Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling","date":"2014-12-11","arxiv_id":"1412.3555","repositories_listed":14,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/music-transformer","title":"Music Transformer","date":"2018-09-12","arxiv_id":"1809.04281","repositories_listed":12,"syntology":{"n":4,"n_ran":2,"n_unverified":2,"n_pointer_only":1}},{"url":"/paper/pop-music-transformer-generating-music-with","title":"Pop Music Transformer: Beat-based Modeling and Generation of Expressive Pop Piano Compositions","date":"2020-02-01","arxiv_id":"2002.00212","repositories_listed":7,"syntology":null},{"url":"/paper/counterpoint-by-convolution","title":"Counterpoint by Convolution","date":"2019-03-18","arxiv_id":"1903.07227","repositories_listed":4,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/enabling-factorized-piano-music-modeling-and","title":"Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset","date":"2018-10-29","arxiv_id":"1810.12247","repositories_listed":4,"syntology":null},{"url":"/paper/rethinking-neural-operations-for-diverse","title":"Rethinking Neural Operations for Diverse Tasks","date":"2021-03-29","arxiv_id":"2103.15798","repositories_listed":3,"syntology":{"n":34,"n_ran":18,"n_unverified":16,"n_pointer_only":0}},{"url":"/paper/gating-revisited-deep-multi-layer-rnns-that-1","title":"Gating Revisited: Deep Multi-layer RNNs That Can Be Trained","date":"2019-11-25","arxiv_id":"1911.11033","repositories_listed":3,"syntology":null},{"url":"/paper/learning-a-latent-space-of-style-aware","title":"Learning Style-Aware Symbolic Music Representations by Adversarial Autoencoders","date":"2020-01-15","arxiv_id":"2001.05494","repositories_listed":2,"syntology":null},{"url":"/paper/improving-polyphonic-music-models-with","title":"Improving Polyphonic Music Models with Feature-Rich Encoding","date":"2019-11-26","arxiv_id":"1911.11775","repositories_listed":2,"syntology":null},{"url":"/paper/r-transformer-recurrent-neural-network","title":"R-Transformer: Recurrent Neural Network Enhanced Transformer","date":"2019-07-12","arxiv_id":"1907.05572","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/deep-learning-for-music","title":"Deep Learning for Music","date":"2016-06-15","arxiv_id":"1606.04930","repositories_listed":2,"syntology":null},{"url":"/paper/frechet-music-distance-a-metric-for","title":"Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation","date":"2024-12-10","arxiv_id":"2412.07948","repositories_listed":1,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/impact-of-time-and-note-duration","title":"Impact of time and note duration tokenizations on deep learning symbolic music modeling","date":"2023-10-12","arxiv_id":"2310.08497","repositories_listed":1,"syntology":null},{"url":"/paper/a-domain-knowledge-inspired-music-embedding","title":"A Domain-Knowledge-Inspired Music Embedding Space and a Novel Attention Mechanism for Symbolic Music Modeling","date":"2022-12-02","arxiv_id":"2212.00973","repositories_listed":1,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":4}},{"url":"/paper/low-rank-constraints-for-fast-inference-in-1","title":"Low-Rank Constraints for Fast Inference in Structured Models","date":"2022-01-08","arxiv_id":"2201.02715","repositories_listed":1,"syntology":null},{"url":"/paper/gates-are-not-what-you-need-in-rnns","title":"Gates Are Not What You Need in RNNs","date":"2021-08-01","arxiv_id":"2108.00527","repositories_listed":1,"syntology":null},{"url":"/paper/popmag-pop-music-accompaniment-generation","title":"PopMAG: Pop Music Accompaniment Generation","date":"2020-08-18","arxiv_id":"2008.07703","repositories_listed":1,"syntology":null},{"url":"/paper/seq-u-net-a-one-dimensional-causal-u-net-for","title":"Seq-U-Net: A One-Dimensional Causal U-Net for Efficient Sequence Modelling","date":"2019-11-14","arxiv_id":"1911.06393","repositories_listed":1,"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/bivariate-beta-lstm","title":"Bivariate Beta-LSTM","date":"2019-05-25","arxiv_id":"1905.10521","repositories_listed":1,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}},{"url":"/paper/diagonal-rnns-in-symbolic-music-modeling","title":"Diagonal RNNs in Symbolic Music Modeling","date":"2017-04-18","arxiv_id":"1704.05420","repositories_listed":1,"syntology":null},{"url":"/paper/sequential-neural-models-with-stochastic","title":"Sequential Neural Models with Stochastic Layers","date":"2016-05-24","arxiv_id":"1605.07571","repositories_listed":1,"syntology":null},{"url":"/paper/modeling-temporal-dependencies-in-high","title":"Modeling Temporal Dependencies in High-Dimensional Sequences: Application to Polyphonic Music Generation and Transcription","date":"2012-06-27","arxiv_id":"1206.6392","repositories_listed":1,"syntology":null}],"syntology_records":10,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}