  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 .
