jazznet: A Dataset of Fundamental Piano Patterns for Music Audio Machine Learning Research
This provides a new dataset for researchers in music audio machine learning, though it is incremental as it focuses on jazz piano patterns.
The paper introduces the jazznet dataset, containing 162,520 labeled piano patterns for music information retrieval, and presents an open-source Pattern Generator using Distance-Based Pattern Structures to facilitate new pattern generation.
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.