PIAST: A Multimodal Piano Dataset with Audio, Symbolic and Text
Bang, Hayeon, Choi, Eunjin, Finch, Megan, Doh, Seungheon, Lee, Seolhee, Lee, Gyeong-Hoon, Nam, Juhan
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Nov-7-2024
- Country:
- Asia > South Korea (0.05)
- Genre:
- Research Report (0.50)
- Industry:
- Leisure & Entertainment (1.00)
- Media > Music (1.00)
- Technology: