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

• URL: http://arxiv.org/abs/2504.15071v1
• Date: Mon, 21 Apr 2025 12:59:40 GMT
• Title: Aria-MIDI: A Dataset of Piano MIDI Files for Symbolic Music Modeling
• Authors: Louis Bradshaw, Simon Colton,
• Abstract summary: We introduce an extensive new dataset of MIDI files, created by transcribing audio recordings of piano performances into their constituent notes.<n>The data pipeline we use is multi-stage, employing a language model to autonomously crawl and score audio recordings from the internet.<n>The resulting dataset contains over one million distinct MIDI files, comprising roughly 100,000 hours of transcribed audio.
• Score: 1.3812010983144802
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Kimi-Audio Technical Report [67.69331679172303]
  Kimi-Audio is an open-source audio foundation model that excels in audio understanding, generation, and conversation.<n>We detail the practices in building Kimi-Audio, including model architecture, data curation, training recipe, inference deployment, and evaluation.
  arXiv  Detail & Related papers  (2025-04-25T15:31:46Z)
• JamendoMaxCaps: A Large Scale Music-caption Dataset with Imputed Metadata [6.230204066837519]
  JamendoMaxCaps is a large-scale music-caption dataset featuring over 362,000 freely licensed instrumental tracks from the Jamendo platform.<n>The dataset includes captions generated by a state-of-the-art captioning model, enhanced with imputed metadata.
  arXiv  Detail & Related papers  (2025-02-11T11:12:19Z)
• PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text [8.382511298208003]
  PIAST (PIano dataset with Audio, Symbolic, and Text) is a piano music dataset. We collected 9,673 tracks from YouTube and added human annotations for 2,023 tracks by music experts. Both include audio, text, tag annotations, and transcribed MIDI utilizing state-of-the-art piano transcription and beat tracking models.
  arXiv  Detail & Related papers  (2024-11-04T19:34:13Z)
• MOSA: Music Motion with Semantic Annotation Dataset for Cross-Modal Music Processing [3.3162176082220975]
  We present the MOSA (Music mOtion with Semantic ) dataset, which contains high quality 3-D motion capture data, aligned audio recordings, and note-by-note semantic annotations of pitch, beat, phrase, dynamic, articulation, and harmony for 742 professional music performances by 23 professional musicians. To our knowledge, this is the largest cross-modal music dataset with note-level annotations to date.
  arXiv  Detail & Related papers  (2024-06-10T15:37:46Z)
• MidiCaps: A large-scale MIDI dataset with text captions [6.806050368211496]
  This work aims to enable research that combines LLMs with symbolic music by presenting, the first openly available large-scale MIDI dataset with text captions. Inspired by recent advancements in captioning techniques, we present a curated dataset of over 168k MIDI files with textual descriptions.
  arXiv  Detail & Related papers  (2024-06-04T12:21:55Z)
• WikiMuTe: A web-sourced dataset of semantic descriptions for music audio [7.4327407361824935]
  We present WikiMuTe, a new and open dataset containing rich semantic descriptions of music. The data is sourced from Wikipedia's rich catalogue of articles covering musical works. We train a model that jointly learns text and audio representations and performs cross-modal retrieval.
  arXiv  Detail & Related papers  (2023-12-14T18:38:02Z)
• Auto-ACD: A Large-scale Dataset for Audio-Language Representation Learning [50.28566759231076]
  We propose an innovative, automatic approach to establish an audio dataset with high-quality captions. Specifically, we construct a large-scale, high-quality, audio-language dataset, named as Auto-ACD, comprising over 1.5M audio-text pairs. We employ LLM to paraphrase a congruent caption for each audio, guided by the extracted multi-modality clues.
  arXiv  Detail & Related papers  (2023-09-20T17:59:32Z)
• Melody transcription via generative pre-training [86.08508957229348]
  Key challenge in melody transcription is building methods which can handle broad audio containing any number of instrument ensembles and musical styles. To confront this challenge, we leverage representations from Jukebox (Dhariwal et al. 2020), a generative model of broad music audio. We derive a new dataset containing $50$ hours of melody transcriptions from crowdsourced annotations of broad music.
  arXiv  Detail & Related papers  (2022-12-04T18:09:23Z)
• A Dataset for Greek Traditional and Folk Music: Lyra [69.07390994897443]
  This paper presents a dataset for Greek Traditional and Folk music that includes 1570 pieces, summing in around 80 hours of data. The dataset incorporates YouTube timestamped links for retrieving audio and video, along with rich metadata information with regards to instrumentation, geography and genre.
  arXiv  Detail & Related papers  (2022-11-21T14:15:43Z)
• Foley Music: Learning to Generate Music from Videos [115.41099127291216]
  Foley Music is a system that can synthesize plausible music for a silent video clip about people playing musical instruments. We first identify two key intermediate representations for a successful video to music generator: body keypoints from videos and MIDI events from audio recordings. We present a Graph$-$Transformer framework that can accurately predict MIDI event sequences in accordance with the body movements.
  arXiv  Detail & Related papers  (2020-07-21T17:59:06Z)
• VGGSound: A Large-scale Audio-Visual Dataset [160.1604237188594]
  We propose a scalable pipeline to create an audio dataset from open-source media. We use this pipeline to curate the VGGSound dataset consisting of more than 210k videos for 310 audio classes. The resulting dataset can be used for training and evaluating audio recognition models.
  arXiv  Detail & Related papers  (2020-04-29T17:46:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.