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Raga Identification In Carnatic Music
- 1. RAGA IDENTIFICATION IN CARNATIC MUSIC G. Varsha Bargavi CSE, 3rd Year
- 2. We will bediscussing… Introduction 1 What automatic Raga identification can provide 2 Characteristics of Carnatic music And Related Terminologies 3 Existing work How automation is not complete 4 System Architecture – The various modules Detailed explanation of every phase 5 Conclusion Acknowledgements and references
- 3. 1.Introduction What automatic Ragaidentification can provide • Computational Musicology Music Databases, Music Analysis, Artificial Production of Music • Music Information Retrieval Classification, clustering, identification, • Raga Identification recognition, perception, cognition, affect, emotions, evaluation as a basis for music search for generating playlists based on themes for evaluating performance and accuracy of raga for answering queries regarding ragas
- 4. 2. Characteristics ofCarnatic Music And related terminologies… • Swara (note) • Octave • Raga • Arohanam and Avarohanam (swara sequence) • Graha Bedham /Shruti Bedham
- 6. Graha Bedham/Shruti Bedham Graha= position Bedham = shift Process (or result of the process) of shifting the Tonic note (śruti) to another note in the raga and arriving at a different raga.
- 7. Śrut Mela i Rāgam # Toni C D E F G A B C D E F G A B C c Shankarabhara 29 C S R2 G3 M1 P D2 N3 S' R2' G3' M1' P' D2' N3' S' ' nam Karaharapriya 22 D S R2 G2 M1 P D2 N2 S' Hanumatodi 08 E S R1 G2 M1 P D1 N2 S' Kalyani 65 F S R2 G3 M2 P D2 N2 S' Harikambhoji 28 G S R2 G3 M1 P D2 N2 S' Natabhairavi 21 A S R2 G2 M1 P D1 N2 S' Invalid -- B S R1 G2 M1 M2 D1 N2 S' Melakarta Shankarabhara 29 C S R2 G3 M1 P D2 N3 S' R2' G3' M1' P' D2' N3' S' ' nam
- 8. 3. Existing Work • Swara Identification and singer identification in Carnatic music • Tansen- Hindustani music raga identification • Melody retrieval and song detection in Western music Limitations… • Some of the work do not consider polyphonic music signals • Not fully automated Fundamental frequency(tonic note) - given as input manually
- 9. 4.System Architecture Input audio Signal Segmentation Frequency Separation Extraction Output Raga Mapping Swara sequences construction Raga database Finite Automata
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- 11. Segmentation and Frequency Extraction F r e q u e n c y Time
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- 13. Applying graha bedham.. Invalid sequence
- 14. Finite Automata To eliminate invalid sequences S .... M1 M2 .... S’ Invalid! M1 M2 ε q1 q2 q3
- 15. Bayesian Network Model Simpleexample- In Raga Detection a1 a2 a3 X1 X2 X3 b21 b32 b22 b11 b33 Y1 Y2 Y3 Xi – Phrases/Gamakas (Random Variables) Yi- Raga ai- Probability associated with Xi bij- conditional probability of yj given Xi Identified raga = Raga ‘Yi’ with highest probability Value
- 16. 5. Conclusion Carnatic MusicRagas- Variables + Constants Constants (Swaras) - distinctly specified using Automata Variables (Phrases) - Represented using associated probabilities and mapped to respective Raga using a probabilistic (Bayesian) network model
- 17. iRaga Imagine... The next version of iPhone having an app called iRaga It can list songs of your favourite raga ... And even create playlists based on a type of emotion , or your mood!
- 18. References 1. Prof. Sambamurthy,South Indian Music, vol. 4, The Indian Music Publishing House, Madras, India, 1982 2. Rajeswari Sridhar and T.V. Geetha, “Swara identification of Carnatic music”, IEEE Computer Society press, proceeding of ICIT 2006 3. Rajeswari Sridhar and T.V. Geetha, “Music Information Retrieval of Carnatic Songs Based on Carnatic Music Singer Identification”, IEEE conference on Computer Science, ICCEE 2008 4. Gaurav Pandey, et al “Tansen: A system for automatic Raga identification” IICAI, 2003 Links- http://www.rasikas.org/ http://news.cnet.com/2009-1001-984695.html http://www.it.iitb.ac.in http://en.wikipedia.org/wiki/Graha_bedham
- 19. Acknowledgements • Mrs. N.Mythili Jagannathan • Mr. S. Rangarajan • Mrs. Uma Maheshwari