Intonation: A Dataset of Quality Vocal Performances Refined by Spectral Clustering on Pitch Congruence
Sanna Wager, George Tzanetakis, Stefan Sullivan, Cheng-i Wang, John Shimmin, Minje Kim, Perry Cook
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. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> 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.
BibTeX
@inproceedings{icassp2019_intonationadatas,
title = {Intonation: A Dataset of Quality Vocal Performances Refined by Spectral Clustering on Pitch Congruence},
author = {Sanna Wager and George Tzanetakis and Stefan Sullivan and Cheng-i Wang and John Shimmin and Minje Kim and Perry Cook},
booktitle = {ICASSP 2019},
year = {2019}
}