Title :
Identification of Carnatic raagas using Hidden Markov Models
Author :
Krishna, A. Srinath ; Rajkumar, P.V. ; Saishankar, K.P. ; John, Mala
Author_Institution :
Cisco Syst., Bangalore, India
Abstract :
Raaga identification is one of the key areas for budding Carnatic musicians and avid listeners. Identification and knowledge of the raaga of a song not only implies knowledge of music but also helps establish the mood of a song. We propose to identify a Carnatic raaga by extracting from the music sample, information about the 12 distinguishable frequencies in an octave. The proposed technique is Specmurt analysis which involves the analysis of a signal in its log-frequency domain. The extracted information is fed to the Hidden Markov Model back-end system where each raaga has its associated model.
Keywords :
acoustic signal processing; feature extraction; hidden Markov models; music; Carnatic musician; Carnatic raaga; Raaga identification; Specmurt analysis; hidden Markov model backend system; information extraction; log frequency domain; music sample extraction; Estimation; Frequency estimation; Harmonic analysis; Hidden Markov models; Mathematical model; Multiple signal classification; Music; hidden markov models; music recognition; raaga; specmurt; swara;
Conference_Titel :
Applied Machine Intelligence and Informatics (SAMI), 2011 IEEE 9th International Symposium on
Conference_Location :
Smolenice
Print_ISBN :
978-1-4244-7429-5
DOI :
10.1109/SAMI.2011.5738857