  Professional vocalists modulate their voice timbre or pitch to make their
vocal performance more expressive. Such fluctuations are called singing
techniques. Automatic detection of singing techniques from audio tracks can be
beneficial to understand how each singer expresses the performance, yet it can
also be difficult due to the wide variety of the singing techniques. A deep
neural network (DNN) model can handle such variety; however, there might be a
possibility that considering the characteristics of the data improves the
performance of singing technique detection. In this paper, we propose PrimaDNN,
a CRNN model with a characteristics-oriented improvement. The features of the
model are: 1) input feature representation based on auxiliary pitch information
and multi-resolution mel spectrograms, 2) Convolution module based on the
Squeeze-and-excitation (SENet) and the Instance normalization. In the results
of J-POP singing technique detection, PrimaDNN achieved the best results of
44.9% at the overall macro-F measure, compared to conventional works. We also
found that the contribution of each component varies depending on the type of
singing technique.
