  Phonation mode is an essential characteristic of singing style as well as an
important expression of performance. It can be classified into four categories,
called neutral, breathy, pressed and flow. Previous studies used voice quality
features and feature engineering for classification. While deep learning has
achieved significant progress in other fields of music information retrieval
(MIR), there are few attempts in the classification of phonation modes. In this
study, a Residual Attention based network is proposed for automatic
classification of phonation modes. The network consists of a convolutional
network performing feature processing and a soft mask branch enabling the
network focus on a specific area. In comparison experiments, the models with
proposed network achieve better results in three of the four datasets than
previous works, among which the highest classification accuracy is 94.58%,
2.29% higher than the baseline.
