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Dhrupad vocal concerts exhibit a temporal evolution through a sequence of homogeneous sections marked by shared rhythmic characteristics. In this work, we address the segmentation of a concert audio's unmetered improvisatory section into musically meaningful segments at the highest time scale. Motivated by the distinct musical properties of the sections and their corresponding acoustic correlates, we compute a number of features for the segment boundary detection task. Both supervised and unsupervised approaches are tested using a dataset of commercial performance recordings that is manually annotated. The dataset is augmented suitably for training and testing of the models to obtain new insights about the relevance of the different rhythmic, melodic and timbral cues in the automatic boundary detection task. We also explore the use of a convolutional neural network trained on mel-scale magnitude spectrograms for the boundary detection task to observe that while the implicit musical cues are largely learned by the network, it is less robust to deviations from training data characteristics. We conclude that it can be rewarding to investigate knowledge driven features on new genres and tasks, both to achieve reasonable performance outcomes given limited datasets and for drawing a deeper understanding of genre characteristics from the acoustical analyses.

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