mlsanityData to 7 Oct 2026

Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey

arXiv 2402.17467 ↗First posted 27 Feb 2024cs.IR cs.AI cs.SDcited by 10

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Abstract (arXiv)

Several adaptations of Transformers models have been developed in various domains since its breakthrough in Natural Language Processing (NLP). This trend has spread into the field of Music Information Retrieval (MIR), including studies processing music data. However, the practice of leveraging NLP tools for symbolic music data is not novel in MIR. Music has been frequently compared to language, as they share several similarities, including sequential representations of text and music. These analogies are also reflected through similar tasks in MIR and NLP. This survey reviews NLP methods applied to symbolic music generation and information retrieval studies following two axes. We first propose an overview of representations of symbolic music adapted from natural language sequential representations. Such representations are designed by considering the specificities of symbolic music. These representations are then processed by models. Such models, possibly originally developed for text and adapted for symbolic music, are trained on various tasks. We describe these models, in particular deep learning models, through different prisms, highlighting music-specialized mechanisms. We finally present a discussion surrounding the effective use of NLP tools for symbolic music data. This includes technical issues regarding NLP methods and fundamental differences between text and music, which may open several doors for further research into more effectively adapting NLP tools to symbolic MIR.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 116 of its 264 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

116 of 264 references.

References · 116

  1. 2017Attention Is All You Needcited by 59,932
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2020Language Models are Few-Shot Learnerscited by 32,199
  4. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 29,915
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  7. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  8. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,874
  9. 2018GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understandingcited by 4,854
  10. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,530
  11. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  12. 2021Zero-Shot Text-to-Image Generationcited by 4,276
38 more
  1. 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,016
  3. 2020Longformer: The Long-Document Transformercited by 3,362
  4. 2015Neural Machine Translation of Rare Words with Subword Unitscited by 2,898
  5. 2019XLNet: Generalized Autoregressive Pretraining for Language Understandingcited by 2,801
  6. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  7. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,341
  8. 2020Transformers are RNNs: Fast Autoregressive Transformers with Linear Attentioncited by 1,979
  9. 2019Transformer-XL: Attentive Language Models Beyond a Fixed-Length Contextcited by 1,830
  10. 2016Enriching Word Vectors with Subword Informationcited by 1,578
  11. 2020Knowledge Distillation: A Surveycited by 1,578
  12. 2019fairseq: A Fast, Extensible Toolkit for Sequence Modelingcited by 1,486
  13. 2019Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Prunedcited by 844
  14. 2019Are Sixteen Heads Really Better than One?cited by 803
  15. 2018Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidatescited by 632
  16. 2019CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Datacited by 544
  17. 2018What you can cram into a single vector: Probing sentence embeddings for linguistic propertiescited by 452
  18. 2018AllenNLP: A Deep Semantic Natural Language Processing Platformcited by 347
  19. 2023Explainability for Large Language Models: A Surveycited by 331
  20. 2019Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasetscited by 298
  21. 2020Evaluation of Text Generation: A Surveycited by 275
  22. 2020XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generationcited by 221
  23. 2018Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Datasetcited by 215
  24. 2020Deep Learning for Text Style Transfer: A Surveycited by 161
  25. 2018A Hierarchical Latent Vector Model for Learning Long-Term Structure in Musiccited by 131
  26. 2021Between words and characters: A Brief History of Open-Vocabulary Modeling and Tokenization in NLPcited by 130
  27. 2020Enabling Language Models to Fill in the Blankscited by 121
  28. 2021Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphscited by 109
  29. 2016Hierarchical Multiscale Recurrent Neural Networkscited by 102
  30. 2016DeepBach: a Steerable Model for Bach Chorales Generationcited by 90
  31. 2021MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Trainingcited by 89
  32. 2020POP909: A Pop-song Dataset for Music Arrangement Generationcited by 83
  33. 2020PopMAG: Pop Music Accompaniment Generationcited by 68
  34. 2018This Time with Feeling: Learning Expressive Musical Performancecited by 67
  35. 2024ChatMusician: Understanding and Generating Music Intrinsically with LLMcited by 67
  36. 2021Video Background Music Generation with Controllable Music Transformercited by 55
  37. 2019LakhNES: Improving multi-instrumental music generation with cross-domain pre-trainingcited by 54
  38. 2020The Jazz Transformer on the Front Line: Exploring the Shortcomings of AI-composed Music through Quantitative Measurescited by 53

50 most cited of 116 All 116 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 2.

10 papers.

  1. 2025A Comprehensive Survey on Generative AI for Video-to-Music Generationcited by 4
  2. 2025Music Audio-Visual Question Answering Requires Specialized Multimodal Designscited by 4
  3. 2024BandCondiNet: Parallel Transformers-based Conditional Popular Music Generation with Multi-View Featurescited by 2
  4. 2026Composer Vector: Style-steering Symbolic Music Generation in a Latent Spacecited by 2
  5. 2024Prevailing Research Areas for Music AI in the Era of Foundation Modelscited by 1
  6. 2024Analyzing Byte-Pair Encoding on Monophonic and Polyphonic Symbolic Music: A Focus on Musical Phrase Segmentationcited by 0
  7. 2025Improving BERT for Symbolic Music Understanding Using Token Denoising and Pianoroll Predictioncited by 0
  8. 2025Global Beats, Local Tongue: Studying Code Switching in K-pop Hits on Billboard Chartscited by 0
  9. 2025Echoes of Humanity: Exploring the Perceived Humanness of AI Musiccited by 0
  10. 2026Can LLMs understand LilyPond? A benchmark for symbolic music generation and understandingcited by 0