@inproceedings{e108e325cb204fde83a4c2f477635b52,
title = "Intonation: A Dataset of Quality Vocal Performances Refined by Spectral Clustering on Pitch Congruence",
abstract = "We introduce the Intonation dataset of amateur vocal performances with a tendency for good intonation, collected from Smule, Inc. The dataset can be used for music information retrieval tasks such as autotuning, query by humming, and singing style analysis. It is available upon request on the Stanford CCRMA DAMP website.1 We describe a semi-supervised approach to selecting the audio recordings from a larger collection of performances based on intonation patterns. The approach can be applied in other situations where a researcher needs to extract a subset of data samples from a large database. A comparison of the Intonation dataset and the remaining collection of performances shows that the two have different intonation behavior distributions.",
keywords = "clustering, dataset, music information retrieval, pitch, singing",
author = "Sanna Wager and George Tzanetakis and Stefan Sullivan and Wang, {Cheng I.} and John Shimmin and Minje Kim and Perry Cook",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 44th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 ; Conference date: 12-05-2019 Through 17-05-2019",
year = "2019",
month = may,
doi = "10.1109/ICASSP.2019.8683554",
language = "English (US)",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "476--480",
booktitle = "2019 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019 - Proceedings",
address = "United States",
}