|Title||:||Motivic Analysis in Carnatic Music|
|Speaker||:||Shrey Dutta (IITM)|
|Details||:||Tue, 25 Aug, 2015 4:00 PM @ BSB 361|
|Abstract:||:||In Carnatic music, a raga is a collective expression of melodies that consists of svaras (ornamented notes) and phrases (aesthetic threads of ornamented notes) that have been formed through the ages, as established by compositions in their core. The phrases that collectively give a raga its identity are called melodic motifs. The melodic motifs are unique to a raga. Therefore, in any rendition of the raga, either compositional or improvisational, these motifs are rendered in order to establish the raga's identity. Different renditions of a motif differs slightly from each other but, they are enough to confuse a time-series matching algorithm. Our goal is to design algorithmic techniques to automatically find these motifs, their different renditions and, then use the regions replete with these motifs to perform raga verification.
Initial work is dedicated towards finding different renditions of melodic motifs in an improvisational form of raga called the alapana. This problem is known as motif spotting. A melodic motif, preselected by a musician, is queried and its different renditions are spotted using a matching algorithm. Following this work, inspired by how listeners identify ragas, automatic discovery of motifs is attempted using certain segments of compositions which are supposed to be rich in motifs. Similar phrases are extracted from different composition lines in a raga. All similar phrases need not be melodic phrases. Some of them could also appear in other ragas thus, violating the uniqueness property of the motifs. Therefore, these non-motif phrases are filtered out if they are found in composition lines of other ragas. Using this approach, various motifs are discovered for 14 ragas confirming that these segments are replete with motifs. Therefore, using these segments of compositions, raga verification is performed. In raga verification, a melody (a single phrase or many phrases stitched together) along with a raga claim is supplied to system. The system confirms or reject the claim. Raga verification is performed by comparing the snippet of audio supplied with various composition lines of the claimed raga. The obtained score is matched against the scores obtained with composition lines of confusing ragas using score-normalization techniques.
Two algorithms for time-series matching are proposed in this work. One is a modification of the exiting algorithm, Rough Longest Common Subsequence (RLCS). Another proposed algorithm, Longest Common Segment Set (LCSS), is completely novel and uses in between matched segments to give a holistic score. This algorithm comes in two forms: hard and soft. Hard-LCSS treats individual matched segments separately irrespective of their lengths and distribution whereas, soft-LCSS can join two or more segments based on their lengths and distribution in order to compute a holistic score. Using the proposed algorithms, an error rate of ~ 12 % is obtained for raga verification on a database consisting of 17 ragas.