Annotated-VocalSet: A Singing Voice Dataset

There are insufficient datasets of singing files that are adequately annotated. One of the available datasets that includes a variety of vocal techniques (n = 17) and several singers (m = 20) with several WAV files (p = 3560) is the VocalSet dataset. However, although several categories, including t...

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Bibliographic Details
Published in:Applied sciences 2022-09, Vol.12 (18), p.9257
Main Authors: Faghih, Behnam, Timoney, Joseph
Format: Article
Language:English
Subjects:
Algorithms
annotated singing dataset
Annotations
Audio data
Automation
Classification
Contours
Datasets
Evaluation
Frequency
Human error
Information processing
Information retrieval
Loudness
Machine learning
monophonic vocal dataset
Musicians & conductors
Resonant frequencies
Signal processing
Singers
Singing
singing dataset
Software
speaking dataset
vocal dataset
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Summary:There are insufficient datasets of singing files that are adequately annotated. One of the available datasets that includes a variety of vocal techniques (n = 17) and several singers (m = 20) with several WAV files (p = 3560) is the VocalSet dataset. However, although several categories, including techniques, singers, tempo, and loudness, are in the dataset, they are not annotated. Therefore, this study aims to annotate VocalSet to make it a more powerful dataset for researchers. The annotations generated for the VocalSet audio files include fundamental frequency contour, note onset, note offset, the transition between notes, note F0, note duration, Midi pitch, and lyrics. This paper describes the generated dataset and explains our approaches to creating and testing the annotations. Moreover, four different methods to define the onset/offset are compared.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12189257