Semantic Tagging of Singing Voices in Popular Music Recordings

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Singing voice is a key sound source in popular music. As recent music streaming and entertainment services call for more intelligent solutions to retrieve songs or evaluate musical characteristics, automatic analysis of popular music targeted to singing voice has been a significant research subject. The majority of studies have focused on quantitative or objective information of singing voice such as pitch, lyrics or singer identity. However, singing voice has a wide variety of dimensions that are somewhat difficult to quantify and therefore we often describe by words. In this article, we address the qualitative analysis of singing voice as a music auto-tagging task that annotates songs with a set of tag words. To this end, we build a music tag dataset dedicated to singing voice. Specifically, we define a vocabulary that describes timbre and singing styles of K-pop vocalists and collect human annotations for individual tracks. We then conduct statistical analysis to understand the global and temporal characteristics of the tag words. Using the dataset, we train a deep neural network model to automatically predict the voice-specific tags from popular music recordings and evaluate the model in different conditions. We discuss the results by comparing them to the statistical analysis of tag words. Finally, we show potential applications of the vocal tagging system in music retrieval, music thumbnailing and singing evaluation.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2020-05
Language
English
Article Type
Article
Citation

IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING, v.28, pp.1656 - 1668

ISSN
2329-9290
DOI
10.1109/taslp.2020.2993893
URI
http://hdl.handle.net/10203/275511
Appears in Collection
GCT-Journal Papers(저널논문)
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