  We investigate methods for the automatic labeling of the taan section, a
prominent structural component of the Hindustani Khayal vocal concert. The taan
contains improvised raga-based melody rendered in the highly distinctive style
of rapid pitch and energy modulations of the voice. We propose computational
features that capture these specific high-level characteristics of the singing
voice in the polyphonic context. The extracted local features are used to
achieve classification at the frame level via a trained multilayer perceptron
(MLP) network, followed by grouping and segmentation based on novelty
detection. We report high accuracies with reference to musician annotated taan
sections across artists and concerts. We also compare the performance obtained
by the compact specialized features with frame-level classification via a
convolutional neural network (CNN) operating directly on audio spectrogram
patches for the same task. While the relatively simple architecture we
experiment with does not quite attain the classification accuracy of the
hand-crafted features, it provides for a performance well above chance with
interesting insights about the ability of the network to learn discriminative
features effectively from labeled data.
