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A Traditional Approach to Symbolic Piano Continuation

arXiv 2509.12267 ↗First posted 13 Sep 2025cs.SD cs.LG cs.MMcited by 0

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Abstract (arXiv)

We present a traditional approach to symbolic piano music continuation for the MIREX 2025 Symbolic Music Generation challenge. While computational music generation has recently focused on developing large foundation models with sophisticated architectural modifications, we argue that simpler approaches remain more effective for constrained, single-instrument tasks. We thus return to a simple, unaugmented next-token-prediction objective on tokenized raw MIDI, aiming to outperform large foundation models by using better data and better fundamentals. We release model weights and code at https://github.com/christianazinn/mirex2025.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 16 references that are in the map. First posted on arXiv 13 Sep 2025.

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References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  3. 2018Music Transformercited by 211
  4. 2025RWKV-7 "Goose" with Expressive Dynamic State Evolutioncited by 141
  5. 2021Scale Efficiently: Insights from Pre-training and Fine-tuning Transformerscited by 106
  6. 2023MuseCoco: Generating Symbolic Music from Textcited by 52
  7. 2022Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generationcited by 49
  8. 2023miditok: A Python package for MIDI file tokenizationcited by 27
  9. 2025Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modelingcited by 10
  10. 2025Adaptable Symbolic Music Infilling with MIDI-RWKVcited by 2

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