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.

In the Indian classical drumming tradition, the different strokes on the tabla are named by spoken syllables(bols) in a case of onomatopoeia. The recitation of a tabla composition using vocalic syllables(bols) plays an important role in the oral tradition of pedagogy in North Indian classical music. Previous studies have considered the phonetic features of isolated bol utterances with the corresponding isolated strokes produced on the tabla. In this work, we investigate the acoustic properties of bol recitation beyond the segmental measurements related to the phones or syllables. The recitation of a tabla composition, apart from conveying the basic score in terms of the sequence of stroke name and onset times, is typically quite expressive in nature, being marked by pitch variations, loudness dynamics and voice quality variations across a sequence or phrase. Given the distinct spaces of acoustic variation of the voice and tabla, we study acoustic-prosodic variations in the recitation and investigate the corresponding (time-aligned) supra-segmental acoustic variations in the drumming. An available large dataset of recordings of selected tabla compositions by an expert tabla player, each aligned with the corresponding bol recitation, is employed in the analyses. We find that while the recitation reliably encodes intensity variations across bols in a cycle, the observed pitch variations are meaningful only for the pitch-varying drum strokes of the left drum.

No abstract