  Metaverse is an interactive world that combines reality and virtuality, where
participants can be virtual avatars. Anyone can hold a concert in a virtual
concert hall, and users can quickly identify the real singer behind the virtual
idol through the singer identification. Most singer identification methods are
processed using the frame-level features. However, expect the singer's timbre,
the music frame includes music information, such as melodiousness, rhythm, and
tonal. It means the music information is noise for using frame-level features
to identify the singers. In this paper, instead of only the frame-level
features, we propose to use another two features that address this problem.
Middle-level feature, which represents the music's melodiousness, rhythmic
stability, and tonal stability, and is able to capture the perceptual features
of music. The timbre feature, which is used in speaker identification,
represents the singers' voice features. Furthermore, we propose a convolutional
recurrent neural network (CRNN) to combine three features for singer
identification. The model firstly fuses the frame-level feature and timbre
feature and then combines middle-level features to the mix features. In
experiments, the proposed method achieves comparable performance on an average
F1 score of 0.81 on the benchmark dataset of Artist20, which significantly
improves related works.
