NCM-Based Raga Classification Using Musical Features

This paper deals with the study of Carnatic raga identification using musical features. In Carnatic music, there are 72 melakartha ragas. Each raga is denoted by musical notes. The musical features of 72 main ragas are extracted. A number of features such as pitch, timbre, tonal, rhythmic features have been discussed with reference to their ability to distinguish different ragas. Due to the intricate nature of Carnatic music, the concept of neutrosophic logic is used to identify each raga. This is because the concept of neutrosophic logic lies in the neutralities present in between truth and false. This creates a component of indeterminacy, which will make raga identification more accurate and smooth. Neutrosophic Cognitive Maps (NCMs) are drawn based on the musical features and solved. Using neutrosophic logic, a reduced set of musical features is arrived for each raga which can be thought of features characterizing the raga. Each raga is classified using a set of musical features which are solutions of NCMs. This paper represents one of the first attempts to classify all 72 melakartha ragas of using neutrosophic logic.

[1]  S Aseervatham,et al.  Study on the Influences of Ragas in Holy Mass Songs Using Neutrosophic Fuzzy Cognitive Maps (NFCMS) , 2013 .

[2]  Khalid M. Amin,et al.  A novel breast tumor classification algorithm using neutrosophic score features , 2016 .

[3]  T R Prashanth,et al.  Note identification in Carnatic Music from Frequency Spectrum , 2011, 2011 International Conference on Communications and Signal Processing.

[4]  R. Sudha,et al.  A System of Tool for Identifying Ragas Using MIDI , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[5]  A. V. Raman The Katapayadi formula and the modern hashing technique , 1997 .

[6]  Hossein Nezamabadi-pour,et al.  An advanced ACO algorithm for feature subset selection , 2015, Neurocomputing.

[7]  R. Sridhar Raga Identification of Carnatic music for Music Information Retrieval , 2009 .

[8]  Yanhui Guo,et al.  A novel image segmentation algorithm based on neutrosophic similarity clustering , 2014, Appl. Soft Comput..

[9]  Anand Raman,et al.  The Ancient Katapayadi Formula And The Modern Hashing Method , 2001 .

[10]  Florentin Smarandache,et al.  FUZZY COGNITIVE MAPS AND NEUTROSOPHIC COGNITIVE MAPS , 2003, math/0311063.

[11]  Bhiksha Raj,et al.  Swara Histogram Based Structural Analysis And Identification Of Indian Classical Ragas , 2013, ISMIR.

[12]  Lie Lu,et al.  Digital Object Identifier (DOI) 10.1007/s00530-002-0065-0 Multimedia Systems , 2003 .

[13]  Feng Liu,et al.  Proceedings of the first international conference on Neutrosophy, neutrosophic logic, neutrosophic set, neutrosophic probability and statistics , 2003, math/0306384.

[14]  Chaitanya Mishra,et al.  TANSEN: A System for Automatic Raga Identification , 2003, IICAI.

[15]  C. Ashbacher Introduction to neutrosophic logic , 2002 .

[16]  Petri Toiviainen,et al.  A Matlab Toolbox for Music Information Retrieval , 2007, GfKl.

[17]  Parag Chordia,et al.  Raag Recognition Using Pitch-Class and Pitch-Class Dyad Distributions , 2007, ISMIR.

[18]  Florentin Smarandache,et al.  Neutrosophy, A New Branch of Philosophy , 2014 .

[19]  I. el-henawy,et al.  A Review on the Applications of Neutrosophic Sets , 2016 .

[20]  Keqin Li,et al.  Toward trustworthy cloud service selection: A time-aware approach using interval neutrosophic set , 2016, J. Parallel Distributed Comput..