PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
November 04, 2024 ยท Declared Dead ยท ๐ NLP4MUSA
"No code URL or promise found in abstract"
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Authors
Hayeon Bang, Eunjin Choi, Megan Finch, Seungheon Doh, Seolhee Lee, Gyeong-Hoon Lee, Juhan Nam
arXiv ID
2411.02551
Category
cs.SD: Sound
Cross-listed
cs.AI,
cs.MM,
eess.AS
Citations
2
Venue
NLP4MUSA
Last Checked
3 months ago
Abstract
While piano music has become a significant area of study in Music Information Retrieval (MIR), there is a notable lack of datasets for piano solo music with text labels. To address this gap, we present PIAST (PIano dataset with Audio, Symbolic, and Text), a piano music dataset. Utilizing a piano-specific taxonomy of semantic tags, we collected 9,673 tracks from YouTube and added human annotations for 2,023 tracks by music experts, resulting in two subsets: PIAST-YT and PIAST-AT. Both include audio, text, tag annotations, and transcribed MIDI utilizing state-of-the-art piano transcription and beat tracking models. Among many possible tasks with the multi-modal dataset, we conduct music tagging and retrieval using both audio and MIDI data and report baseline performances to demonstrate its potential as a valuable resource for MIR research.
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