mlsanityData to 14 Oct 2026

Exploring Tokenization Methods for Multitrack Sheet Music Generation

arXiv 2410.17584 ↗First posted 23 Oct 2024cs.SD cs.AI eess.AScited by 3

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Abstract (arXiv)

This study explores the tokenization of multitrack sheet music in ABC notation, introducing two methods--bar-stream and line-stream patching. We compare these methods against existing techniques, including bar patching, byte patching, and Byte Pair Encoding (BPE). In terms of both computational efficiency and the musicality of the generated compositions, experimental results show that bar-stream patching performs best overall compared to the others, which makes it a promising tokenization strategy for sheet music generation.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 5 of its 13 references that are in the map. First posted on arXiv 23 Oct 2024.

Builds on

5 of 13 references.

References · 5

  1. 2016Music transcription modelling and composition using deep learningcited by 36
  2. 2024MuPT: A Generative Symbolic Music Pretrained Transformercited by 33
  3. 2024Beyond Language Models: Byte Models are Digital World Simulatorscited by 22
  4. 2023TunesFormer: Forming Irish Tunes with Control Codes by Bar Patchingcited by 20
  5. 2024CLaMP 2: Multimodal Music Information Retrieval Across 101 Languages Using Large Language Modelscited by 12

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 2; 2026: 1.

3 papers.

  1. 2025NotaGen: Advancing Musicality in Symbolic Music Generation with Large Language Model Training Paradigmscited by 30
  2. 2025Via Score to Performance: Efficient Human-Controllable Long Song Generation with Bar-Level Symbolic Notationcited by 3
  3. 2026Agogic: Performance-Timed Music Tokens for LLM-Native Text-to-Symbolic-Music Generationcited by 0