# POP909: A Pop-song Dataset for Music Arrangement Generation

arXiv: https://arxiv.org/abs/2008.07142 · HTML: https://mlsanity.com/paper/2008.07142/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2020.

## Built on by (most cited first, 30 of 83)

- [Symbolic Music Generation with Non-Differentiable Rule Guided Diffusion](https://mlsanity.com/paper/2402.14285/) (2024)
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- [MusicMagus: Zero-Shot Text-to-Music Editing via Diffusion Models](https://mlsanity.com/paper/2402.06178/) (2024)
- [Controllable deep melody generation via hierarchical music structure representation](https://mlsanity.com/paper/2109.00663/) (2021)
- [Video Background Music Generation: Dataset, Method and Evaluation](https://mlsanity.com/paper/2211.11248/) (2022)
- [Whole-Song Hierarchical Generation of Symbolic Music Using Cascaded Diffusion Models](https://mlsanity.com/paper/2405.09901/) (2024)
- [Training-Free Guidance Beyond Differentiability: Scalable Path Steering with Tree Search in Diffusion and Flow Models](https://mlsanity.com/paper/2502.11420/) (2025)
- [DadaGP: A Dataset of Tokenized GuitarPro Songs for Sequence Models](https://mlsanity.com/paper/2107.14653/) (2021)
- [Byte Pair Encoding for Symbolic Music](https://mlsanity.com/paper/2301.11975/) (2023)
- [MuChin: A Chinese Colloquial Description Benchmark for Evaluating Language Models in the Field of Music](https://mlsanity.com/paper/2402.09871/) (2024)
- [PDMX: A Large-Scale Public Domain MusicXML Dataset for Symbolic Music Processing](https://mlsanity.com/paper/2409.10831/) (2024)
- [Frechet Music Distance: A Metric For Generative Symbolic Music Evaluation](https://mlsanity.com/paper/2412.07948/) (2024)
- [BERT-like Pre-training for Symbolic Piano Music Classification Tasks](https://mlsanity.com/paper/2107.05223/) (2021)
- [PianoBART: Symbolic Piano Music Generation and Understanding with Large-Scale Pre-Training](https://mlsanity.com/paper/2407.03361/) (2024)
- [Structure-Enhanced Pop Music Generation via Harmony-Aware Learning](https://mlsanity.com/paper/2109.06441/) (2021)
- [Pop2Piano : Pop Audio-based Piano Cover Generation](https://mlsanity.com/paper/2211.00895/) (2022)
- [Natural Language Processing Methods for Symbolic Music Generation and Information Retrieval: a Survey](https://mlsanity.com/paper/2402.17467/) (2024)
- [Structured Multi-Track Accompaniment Arrangement via Style Prior Modelling](https://mlsanity.com/paper/2310.16334/) (2023)
- [YM2413-MDB: A Multi-Instrumental FM Video Game Music Dataset with Emotion Annotations](https://mlsanity.com/paper/2211.07131/) (2022)
- [The GigaMIDI Dataset with Features for Expressive Music Performance Detection](https://mlsanity.com/paper/2502.17726/) (2025)
- [Audio-to-symbolic Arrangement via Cross-modal Music Representation Learning](https://mlsanity.com/paper/2112.15110/) (2021)
- [Msanii: High Fidelity Music Synthesis on a Shoestring Budget](https://mlsanity.com/paper/2301.06468/) (2023)
- [Impact of time and note duration tokenizations on deep learning symbolic music modeling](https://mlsanity.com/paper/2310.08497/) (2023)
- [An Order-Complexity Aesthetic Assessment Model for Aesthetic-aware Music Recommendation](https://mlsanity.com/paper/2402.08300/) (2024)
- [Learning long-term music representations via hierarchical contextual constraints](https://mlsanity.com/paper/2202.06180/) (2022)
- [MeloForm: Generating Melody with Musical Form based on Expert Systems and Neural Networks](https://mlsanity.com/paper/2208.14345/) (2022)
- [N-Gram Unsupervised Compoundation and Feature Injection for Better Symbolic Music Understanding](https://mlsanity.com/paper/2312.08931/) (2023)
- [Structure-informed Positional Encoding for Music Generation](https://mlsanity.com/paper/2402.13301/) (2024)

## Abstract (arXiv)

Music arrangement generation is a subtask of automatic music generation, which involves reconstructing and re-conceptualizing a piece with new compositional techniques. Such a generation process inevitably requires reference from the original melody, chord progression, or other structural information. Despite some promising models for arrangement, they lack more refined data to achieve better evaluations and more practical results. In this paper, we propose POP909, a dataset which contains multiple versions of the piano arrangements of 909 popular songs created by professional musicians. The main body of the dataset contains the vocal melody, the lead instrument melody, and the piano accompaniment for each song in MIDI format, which are aligned to the original audio files. Furthermore, we provide the annotations of tempo, beat, key, and chords, where the tempo curves are hand-labeled and others are done by MIR algorithms. Finally, we conduct several baseline experiments with this dataset using standard deep music generation algorithms.