- A. Vidwans, K. Ganguli and P. Rao, "Classification of Indian Classical Vocal Styles from Melodic Contours", Proc. of 2nd CompMusic Workshop, Jul 2012, Istanbul, Turkey. [pdf] [slides] [video]
- Abstract: A prominent categorization of Indian classical music is the Hindustani and Carnatic traditions, the two styles having evolved under distinctly different historical and cultural influences. Both styles are grounded in the melodic and rhythmic framework of raga and tala. The styles differ along dimensions such as instrumentation, aesthetics and voice production. In particular, Carnatic music is perceived as being more ornamented. The hypothesis that style distinctions are embedded in the melodic contour is validated via subjective classification tests. Melodic features representing the distinctive characteristics are extracted from the audio. Previous work based on the extent of stable pitch regions is supported by measurements of musicians’ annotations of stable notes. Further, a new feature is introduced that captures the presence of specific pitch modulations characteristic of ornamentation in Indian classical music. The combined features show high classification accuracy on a database of vocal music of prominent artistes. The miss-classifications are seen to match actual listener confusions.
- A. Vidwans and P. Rao, "Identifying Indian Classical Music Styles using Melodic Contours", Proc. of Frontiers of Research on Speech and Music (FRSM), Jan 2012, Gurgaon, India. [pdf]
- Abstract: Hindustani and Carnatic classical vocal styles are grounded in the melodic and rhythmic framework of raga and tala. The styles differ along dimensions such as structure of a performance, aesthetics, voice production and instrumentation. In this work we explore methods to distinguish the two prominent styles by analyzing the extracted melodic contour from audio segments of vocal performances. The assumption that style distinctions are represented in the melodic contour is validated via human listening tests. The presented features can be applied to achieve automatic classification of style.
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