Characterization of Intonation in Karṇāṭaka Music by Parametrizing Context-based Svara Distributions
نویسندگان
چکیده
Intonation is a fffndamental mffsic concept that has a special relefiance in Indian art mffsic. It is characteristic of the rāga and intrinsic to the mffsical expression of the performer. Describing intonation is of importance to sefieral information retriefial tasks like the defielopment of rāga and artist similarity measffres. In offr prefiioffs flork, fle proposed a compact representation of intonation based on the parametrization of the pitch histogram of a performance and demonstrated the ffseffflness of this representation throffgh an exploratifie rāga recognition task in flhich fle classi ed 42 fiocal performances belonging to 3 rāgas ffsing parameters of a single sfiara. In this paper, fle extend this representation to employ context-based sfiara distribfftions, flhich are obtained flith a di erent approach to nd the pitches belonging to each sfiara. We qffantitatifiely compare this method to offr prefiioffs one, discffss the adfiantages, and the necessary melodic analysis to be carried offt in ffftffre.
منابع مشابه
Intonation analysis of ragas in Carnatic music
Intonation is a fundamental music concept that has a special relevance in Indian art music. It is characteristic of a raga and key to the musical expression of the artist. Describing intonation is of importance to several music information retrieval tasks such as developing similarity measures based on ragas and artists. In this paper, we first assess raga intonation qualitatively by analysing ...
متن کاملCharacterization of Intonation in Carnatic Music by Parametrizing Pitch Histograms
Intonation is an important concept in Carnatic music that is characteristic of a raaga, and intrinsic to the musical expression of a performer. In this paper we approach the description of intonation from a computational perspective, obtaining a compact representation of the pitch track of a recording. First, we extract pitch contours from automatically selected voice segments. Then, we obtain ...
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