# jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research

arXiv: https://arxiv.org/abs/2302.08632 · HTML: https://mlsanity.com/paper/2302.08632/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 17 Feb 2023.

## Builds on (most cited first, 6 of 6)

- [Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders](https://mlsanity.com/paper/1704.01279/) (2017)
- [FMA: A Dataset For Music Analysis](https://mlsanity.com/paper/1612.01840/) (2016)
- [Very Deep Convolutional Neural Networks for Raw Waveforms](https://mlsanity.com/paper/1610.00087/) (2016)
- [Learning Features of Music from Scratch](https://mlsanity.com/paper/1611.09827/) (2016)
- [Convolutional Recurrent Neural Networks for Music Classification](https://mlsanity.com/paper/1609.04243/) (2016)
- [A Functional Taxonomy of Music Generation Systems](https://mlsanity.com/paper/1812.04186/) (2018)

## Built on by (most cited first, 3 of 3)

- [Unsupervised Musical Object Discovery from Audio](https://mlsanity.com/paper/2311.07534/) (2023)
- [Knowledge-based Multimodal Music Similarity](https://mlsanity.com/paper/2306.12249/) (2023)
- [Audio-FLAN: An Instruction-Following Dataset for Unified Audio Understanding and Generation of Speech, Music, and Sound](https://mlsanity.com/paper/2502.16584/) (2025)

## Abstract (arXiv)

This paper introduces the jazznet Dataset, a dataset of fundamental jazz piano music patterns for developing machine learning (ML) algorithms in music information retrieval (MIR). The dataset contains 162520 labeled piano patterns, including chords, arpeggios, scales, and chord progressions with their inversions, resulting in more than 26k hours of audio and a total size of 95GB. The paper explains the dataset's composition, creation, and generation, and presents an open-source Pattern Generator using a method called Distance-Based Pattern Structures (DBPS), which allows researchers to easily generate new piano patterns simply by defining the distances between pitches within the musical patterns. We demonstrate that the dataset can help researchers benchmark new models for challenging MIR tasks, using a convolutional recurrent neural network (CRNN) and a deep convolutional neural network. The dataset and code are available via: https://github.com/tosiron/jazznet.