# A database linking piano and orchestral MIDI scores with application to automatic projective orchestration

arXiv: https://arxiv.org/abs/1810.08611 · HTML: https://mlsanity.com/paper/1810.08611/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Oct 2018.

## Built on by (most cited first, 11 of 11)

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- [The GigaMIDI Dataset with Features for Expressive Music Performance Detection](https://mlsanity.com/paper/2502.17726/) (2025)
- [Nested Music Transformer: Sequentially Decoding Compound Tokens in Symbolic Music and Audio Generation](https://mlsanity.com/paper/2408.01180/) (2024)
- [SymphonyGen: 3D Hierarchical Orchestral Generation with Controllable Harmony Skeleton](https://mlsanity.com/paper/2604.25498/) (2026)
- [Evaluating Co-Creativity using Total Information Flow](https://mlsanity.com/paper/2402.06810/) (2024)
- [MMT-BERT: Chord-aware Symbolic Music Generation Based on Multitrack Music Transformer and MusicBERT](https://mlsanity.com/paper/2409.00919/) (2024)
- [Latent Space Disentanglement via Activation Steering for Interpretable Attribute Control in Symbolic Music Generation](https://mlsanity.com/paper/2605.31295/) (2026)
- [MusPyExpress: Extending MusPy with Enhanced Expression Text Support](https://mlsanity.com/paper/2608.21678/) (2026)
- [Unsupervised Lead Sheet Generation via Semantic Compression](https://mlsanity.com/paper/2310.10772/) (2023)
- [Amadeus: Autoregressive Model with Bidirectional Attribute Modelling for Symbolic Music](https://mlsanity.com/paper/2508.20665/) (2025)

## Abstract (arXiv)

This article introduces the Projective Orchestral Database (POD), a collection of MIDI scores composed of pairs linking piano scores to their corresponding orchestrations. To the best of our knowledge, this is the first database of its kind, which performs piano or orchestral prediction, but more importantly which tries to learn the correlations between piano and orchestral scores. Hence, we also introduce the projective orchestration task, which consists in learning how to perform the automatic orchestration of a piano score. We show how this task can be addressed using learning methods and also provide methodological guidelines in order to properly use this database.