Annotated-VocalSet: A Singing Voice Dataset

Featured Application Some of the applications of this study are singing and notes alignment, singing and lyrics alignment, singing analysis, voice analysis, singing assessment, singing information retrieval, evaluating pitch detection algorithms, evaluating note extraction algorithms, evaluating onset detection algorithms, score following, and evaluating pitch contour smoother algorithms. 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.