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Musical melodies contain hierarchically organized events, where some events are more salient than others, acting as melodic landmarks. In Hindustani music melodies, an important landmark is the occurrence of a nyas. Occurrence of nyas is crucial to build and sustain the format of a rag and mark the boundaries of melodic motifs. Detection of nyas segments is relevant to tasks such as melody segmentation, motif discovery and rag recognition. However, detection of nyas segments is challenging as these segments do not follow explicit set of rules in terms of segment length, contour characteristics, and melodic context. In this paper we propose a method for the automatic detection of nyas segments in Hindustani music melodies. It consists of two main steps: a segmentation step that incorporates domain knowledge in order to facilitate the placement of nyas boundaries, and a segment classification step that is based on a series of musically motivated pitch contour features. The proposed method obtains significant accuracies for a heterogeneous data set of 20 audio music recordings containing 1257 nyas svar occurrences and total duration of 1.5 hours. Further, we show that the proposed segmentation strategy significantly improves over a classical piece-wise linear segmentation approach.
The code for nyas segmentation can be found here.
The dataset used in the experiments can be found here.