  Singing techniques are used for expressive vocal performances by employing
temporal fluctuations of the timbre, the pitch, and other components of the
voice. Their classification is a challenging task, because of mainly two
factors: 1) the fluctuations in singing techniques have a wide variety and are
affected by many factors and 2) existing datasets are imbalanced. To deal with
these problems, we developed a novel audio feature learning method based on
deformable convolution with decoupled training of the feature extractor and the
classifier using a class-weighted loss function. The experimental results show
the following: 1) the deformable convolution improves the classification
results, particularly when it is applied to the last two convolutional layers,
and 2) both re-training the classifier and weighting the cross-entropy loss
function by a smoothed inverse frequency enhance the classification
performance.
