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

TL;DR

Jazznet is a comprehensive dataset of jazz piano patterns designed to facilitate machine learning research in music information retrieval, featuring a large collection of labeled patterns and an innovative pattern generation method.

Contribution

The paper introduces the jazznet dataset with over 162,000 labeled jazz piano patterns and a novel Distance-Based Pattern Structures method for generating new patterns.

Findings

Dataset enables benchmarking of MIR models.

CRNN and deep CNN achieve promising results on jazznet.

Open-source tools support pattern generation and research.

Abstract

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.