Carnatic Raag Classification

Part of my research project for the last semester was building a Carnatic raag database. Once a substantial raag database was ready, we ran a number of experiments to check how well PCDs (pitch-class distribution) and PCDDs (pitch-class dyad distribution) perform the task of classifying the raags. It turned out that PCDs and PCDDs are very effective for this task. We achieved a 92.5% classification accuracy on 30 target raags, using a Bayesian Classifier. This shows that though raags in Carnatic music are different from raags in North Indian Classical music - in melodic, presentation and ornamentation, PCDs and PCDDs are still effective ( Using PCDs and PCDDs for North Indian raag classification is described in Dr.Parag Chordia's ISMIR '07 paper)

The Database :
The table below shows the various raags and their corresponding scale degree:


The following list describes the database audio. Each entry is a separate audio file with its tonic frequency and artist information included in the file name. The list also mentions the type of each of the audio file and its duration.

(Artists Legends: AKC-AKC Natarajan; KVN-Palghat KV Narayanaswamy; DKP-DK Pattammal; DKJ-DK Jayaraman; Nedunuri-Nedunuri Krishnamurthy; SSI-Semmangudi Srinivasa Iyer; TNK-Prof TN Krishnan; MS-MS Subbulakshmi; GNB-GN Balasubramanian; MMI-Madurai Mani Iyer; Sanjay-Sanjay Subrahmanyan;TNS-Madurai TN Seshagopalan; NS-Neyveli Santhanagopalan; Ramani-Flute Ramani;Kadri-Kadri Gopalnath; Ravikiran-Chitraveena Ravikiran; Hari - Shenkottai Hari )

1-Karaharapriya-AKC-307.wav 6:38 Clarinet
1-Karaharapriya-KVN-254.wav 8:01 Male Vocal
2-Kalyani-DKP-308.wav 6:37 Female Vocal
2-Kalyani-Nedunuri-262.wav 7:04 Male Vocal
3-Todi-SSI-260.wav 6:22 Male Vocal
3-Todi-TNK-260.wav 4:15 Violin
4-Sankarabharnam-MS-379.wav 10:39 Female Vocal
4-Sankarabharnam-SSI-265.wav 18:55 Male Vocal
5-Shanmugapriya-NS-274.wav 14:35 Male Vocal
5-Shanmugapriya-Nedunuri-263.wav 1:58 Male Vocal
6-Nattakurinji-GNB-251.wav 10:43 Male Vocal
6-Nattakurinji-Sanjay-293.wav 11:55 Male Vocal
6-Nattakurinji-Sanjay-violin-293.wav 4:30 Violin
7-Kambhoji-Lalgudi-260.wav 7:50 Violin
7-Kambhoji-Nedunuri-260.wav 17:41 Male Vocal
8-Mayamalavagowla-NS-273.wav 0:42 Male Vocal
9-Keeravani-MMI-266.wav 13:24 Male Vocal
10-SimhendraMadhyamam-270.wav 10:31 Male Vocal
11-Khamas-GNB-254.wav 8:27 Male Vocal
11-Khamas-Hari-263.wav 14:28 Male Vocal
12-Hamsadwani-Nedunuri-263.wav 1:35 Male Vocal
13-Mohanam-DKJ-283.wav 15:49 Male Vocal
13-Mohanam-DKP-297.wav 6:23 Female Vocal
14-Bilahari-HydBros-279.wav 1:35 Male Vocal
14-Bilahari-Violin-279.wav 3:37 Violin
15-Nalinakanthi-Violin-283.wav 1:24 Violin
16-Sahana-TNS-254.wav 11:25 Male Vocal
17-Bhairavi-Ramani-323.wav 18:59 Bamboo Flute
17-Bhairavi-SSI-262.wav 6:35 Male Vocal
18-Sriranjani-Kadri-243.wav 3:40 Saxophone
19-AnandhaBhairavi-SSI-262.wav 5:33 Male Vocal
20-Atana-NS-272.wav 1:10 Male Vocal
21-Dwijavanti-SSI-266.wav 1:43 Male Vocal
22-Dhanyasi-KVN-267.wav 10:15 Male Vocal
23-Hindolam-Flute-305.wav 5:50 Bamboo Flute
24-Varali-Vocal-267.wav 4:30 Male Vocal
25-Reethigowlai-Ravikiran-1-263.wav 1:08 Chitraveena
25-Reethigowlai-Ravikiran.wav 1:30 Chitraveena
26-Abheri-NS-273.wav 1:40 Male Vocal
27-Madhyamavathi-Ramani-325.wav 5:55 Bamboo Flute
28-Kaanada-Flute-195.wav 1:15 Bamboo Flute
29-Purvikalyani-SSI-254.wav 10:11 Male Vocal
30-Pantuvarali-Sanjay-290.wav 6:19 Male Vocal


Pitch Tracking :
YIN algorithm was used to compute the Pitch Class Distribution (PCDs) of the audio files. Since the tonic frequency varies between recordings, all the audio files' tonic was manually annotated. The figures below show the discriminative power of a single scale degree. This is boxplot of scale degree D across all target ragas :



Here is the boxplot of the scale degree Eb :

Why machine musician ?

I'll first try giving my thoughts on this question before getting into explaining my work (This in fact is one question which many of my friends keep asking me everyday). This is my thought :

Over the years at high school, at my undergrad univ, at work and here at Georgia Tech, I have met so many people who have at some point of time learned 2-3 years of some kind of formal music - but never continued with that for different reasons. Taking music from these 2-3 years learning level to a concert performance level is definitely not easy. I feel that taking one's music to the next level needs collaboration - this is the time one needs to actually "jam" and practice with friends or fellow musicians. Not everyone gets the right people to do this. And here comes the need for a "machine musician" - A software application with which you can jam together, practice and produce good music. This application can listen to you, respond to you and even correct the mistakes in your music !

Machine Musician

My research with Dr.Parag Chordia is about using MIR(Music Information Retrieval) techniques to train the computer to listen and emulate a human musician. The research started with trying to make the computer listen and comprehend the different strokes(about 13 of them) of the mridangam, the primary South Indian Drum. It was a semester long effort and in the subsequent posts, I'll post the results and the explanation of that.

I am currently working on a live performance piece out of this to be presented at the Listening Machines concert here at Georgia Tech. It would be a 7-8 minutes piece in the traditional "tani avarthanam" or "jugal bandhi" style.