Analysis and Detection of Singing Techniques in Repertoires of J-POP Solo Singers

Autor:	Yamamoto, Yuya, Nam, Juhan, Terasawa, Hiroko
Rok vydání:	2022
Předmět:	Computer Science - Sound Computer Science - Digital Libraries Computer Science - Information Retrieval Computer Science - Multimedia Electrical Engineering and Systems Science - Audio and Speech Processing
Druh dokumentu:	Working Paper
Popis:	In this paper, we focus on singing techniques within the scope of music information retrieval research. We investigate how singers use singing techniques using real-world recordings of famous solo singers in Japanese popular music songs (J-POP). First, we built a new dataset of singing techniques. The dataset consists of 168 commercial J-POP songs, and each song is annotated using various singing techniques with timestamps and vocal pitch contours. We also present descriptive statistics of singing techniques on the dataset to clarify what and how often singing techniques appear. We further explored the difficulty of the automatic detection of singing techniques using previously proposed machine learning techniques. In the detection, we also investigate the effectiveness of auxiliary information (i.e., pitch and distribution of label duration), not only providing the baseline. The best result achieves 40.4% at macro-average F-measure on nine-way multi-class detection. We provide the annotation of the dataset and its detail on the appendix website 0 . Comment: Accepted at ISMIR 2022, appendix website: https://yamathcy.github.io/ISMIR2022J-POP/
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.17367 Zobrazit plný text záznamu View this record from Arxiv