Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling

We introduce an extensive new dataset of MIDI files, created by transcribing audio recordings of piano performances into their constituent notes. The data pipeline we use is multi-stage, employing a language model to autonomously crawl and score audio recordings from the internet based on their metadata, followed by a stage of pruning and segmentation using an audio classifier. The resulting dataset contains over one million distinct MIDI files, comprising roughly 100,000 hours of transcribed audio. We provide an in-depth analysis of our techniques, offering statistical insights, and investigate the content by extracting metadata tags, which we also provide. Dataset available at https://github.com/loubbrad/aria-midi.

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Publication Details

Journal
ArXiv.org
Published
2025-04-21
Primary Topic
Music and Audio Processing
Type
article
Field-Weighted Citation Impact
0.00

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article

Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling

ArXiv.org
Music and Audio Processing
article

Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling

article en

Abstract

We introduce an extensive new dataset of MIDI files, created by transcribing audio recordings of piano performances into their constituent notes. The data pipeline we use is multi-stage, employing a language model to autonomously crawl and score audio recordings from the internet based on their metadata, followed by a stage of pruning and segmentation using an audio classifier. The resulting dataset contains over one million distinct MIDI files, comprising roughly 100,000 hours of transcribed audio. We provide an in-depth analysis of our techniques, offering statistical insights, and investigate the content by extracting metadata tags, which we also provide. Dataset available at https://github.com/loubbrad/aria-midi.

ArXiv.org
Universidad de Londres (MX)
UK Research and Innovation, Ministry of Science and ICT, South Korea, Engineering and Physical Sciences Research Council
Openalex Percentile: Top 45%
Music and Audio Processing
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