The Stride Piano Dataset is a comprehensive collection of audio tracks paired with detailed metadata, meticulously crafted to drive progress in machine learning applications across various domains. This dataset offers a fascinating exploration into the world of stride piano music, showcasing the distinctive rhythmic and harmonic techniques characteristic of this iconic jazz piano style.

It encompasses the dynamic sound of stride piano, celebrated for its lively syncopation, intricate improvisation, and virtuosic performances. Originating in the early 20th century, stride piano has left an indelible mark on jazz music, with its distinctive blend of ragtime influences and improvisational prowess. Each audio track within this dataset captures the infectious energy and expressive depth of stride piano, providing a wealth of material for exploration and analysis.

Accompanying the audio tracks are detailed metadata annotations, offering insights into musical structure, instrumentation, key signatures, tempo variations, timestamps, and more. This comprehensive metadata empowers machine learning models to comprehend the nuances of stride piano composition and performance, facilitating tasks such as generative music composition, music information retrieval (MIR), source separation, and beyond. The Stride Piano Dataset serves as a valuable resource for researchers, musicians, and developers interested in delving into the rich tradition and innovative potential of stride piano within the realm of machine learning, unlocking new avenues for creativity and exploration in the dynamic landscape of jazz music.