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Raga Identification In Carnatic Music

This presentation explains, theoretically, how Carnatic Ragas can be identified by the computer, given a sample audio input. It is based on the principles of Graha Bedham, Theory of Automata and Bayesian Networks.

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Raga Identification In Carnatic Music

  2. 2. We will be discussing… 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. 3. 1.IntroductionWhat automatic Raga identification 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. 4. 2. Characteristics of Carnatic Music And related terminologies… • Swara (note) • Octave • Raga • Arohanam and Avarohanam (swara sequence) • Graha Bedham /Shruti Bedham
  5. 5. Graha Bedham/Shruti BedhamGraha = positionBedham = shift Process (or result of the process) ofshifting the Tonic note (śruti) to anothernote in the raga and arriving at a differentraga.
  6. 6. Śrut Mela i Rāgam # Toni C D E F G A B C D E F G A B C cShankarabhara 29 C S R2 G3 M1 P D2 N3 S R2 G3 M1 P D2 N3 S namKaraharapriya 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 SHarikambhoji 28 G S R2 G3 M1 P D2 N2 SNatabhairavi 21 A S R2 G2 M1 P D1 N2 S Invalid -- B S R1 G2 M1 M2 D1 N2 S MelakartaShankarabhara 29 C S R2 G3 M1 P D2 N3 S R2 G3 M1 P D2 N3 S nam
  7. 7. 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
  8. 8. 4.System ArchitectureInputaudio Signal Segmentation Frequency Separation Extraction Output Raga Mapping Swara sequences construction Raga database Finite Automata
  9. 9. Signal Separation
  10. 10. Segmentation and FrequencyExtraction F r e q u e n c y Time
  11. 11. Swara Sequences Construction
  12. 12. Applying graha bedham.. Invalid sequence
  13. 13. Finite Automata To eliminate invalid sequences S .... M1 M2 .... S’ Invalid! M1 M2 ε q1 q2 q3
  14. 14. Bayesian Network ModelSimple example- 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
  15. 15. 5. ConclusionCarnatic Music Ragas- Variables + ConstantsConstants (Swaras) - distinctly specified usingAutomataVariables (Phrases) - Represented using associatedprobabilities and mapped to respective Raga using aprobabilistic (Bayesian) network model
  16. 16. 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!
  17. 17. References1. Prof. Sambamurthy, South Indian Music, vol. 4, The IndianMusic Publishing House, Madras, India, 19822. Rajeswari Sridhar and T.V. Geetha, “Swara identification of Carnatic music”, IEEEComputer Society press, proceeding of ICIT 20063. Rajeswari Sridhar and T.V. Geetha, “Music InformationRetrieval of Carnatic Songs Based on Carnatic Music SingerIdentification”, IEEE conference on Computer Science, ICCEE 20084. Gaurav Pandey, et al “Tansen: A system for automatic Raga identification” IICAI,2003Links-
  18. 18. Acknowledgements• Mrs. N. Mythili Jagannathan• Mr. S. Rangarajan• Mrs. Uma Maheshwari