Identifying Ragas in Indian Music

Kumar, Vijay; Pandya, Harit; Jawahar, C. V.

doi:10.1109/icpr.2014.142

Cited by 40 publications

(16 citation statements)

References 9 publications

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“…It has further been observed, that very less work has been done on Indian music (Gaikwad et al 2014;Jothilakshmi and Kathiresan 2012;Kini et al 2011;Krishnaswamy 2003;Kumar et al 2014;Nagavi and Bhajantri 2011;Rao 2012), especially in South Indian Tamil music (Betsy and Bhalke 2015). Thus, the next sections of the paper presents a novel feature extraction scheme for automatic music genre classification of Indian Tamil music, that include FrFT based MFCC features or fractional MFCC using two classifiers namely KNN and SVM.…”

Section: Literature Surveymentioning

confidence: 93%

Automatic genre classification of Indian Tamil and western music using fractional MFCC

Rajesh

Bhalke

2016

Int J Speech Technol

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This paper presents the automatic genre classification of Indian Tamil music and western music using timbral features and fractional Fourier transform (FrFT) based Mel frequency cepstral coefficient (MFCC) features. The classifier model for the proposed system has been built using K-nearest neighbours and support vector machine (SVM) classifiers. In this work, the performance of various features extracted from music excerpts have been analyzed, to identify the appropriate feature descriptors for the two major genres of Indian Tamil music, namely classical music (Carnatic based devotional hymn compositions) and folk music. The results have shown that the feature combination of spectral roll off, spectral flux, spectral skewness and spectral kurtosis, combined with fractional MFCC features, outperforms all other feature combinations, to yield a higher classification accuracy of 96.05 %, as compared to the accuracy of 84.21 % with conventional MFCC. It has also been observed, that the FrFT based MFCC, with timbral features and SVM, efficiently classifies the two western genres of rock and classical music, from the GTZAN dataset, with fewer features and a higher classification accuracy of 96.25 %, as compared to the classification accuracy of 80 % with conventional MFCC.

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Section: Literature Surveymentioning

confidence: 93%

Automatic genre classification of Indian Tamil and western music using fractional MFCC

Rajesh

Bhalke

2016

Int J Speech Technol

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“…Machine learning has been used in the Indian classical music over the past decade to study multitude of concepts. For example, classifying recordings from YouTube based on the swara using random forest algorithm [13], using pitch information in music signals [14], and a vector based classification model [15], similar to text classification model. Identifying tonic of a music from multi-pitch analysis of the given audio was also given as a classification problem [16].…”

Section: Related Workmentioning

confidence: 99%

ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert

Bagavathi

Krishnan

Shakkottai³

et al. 2019

2019 18th IEEE International Conference on Machine Learning and Applications (ICMLA)

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South Indian classical music (Carnatic music) is best consumed through live concerts. A carnatic recital requires meticulous planning accounting for several parameters like the performers' repertoire, composition variety, musical versatility, thematic structure, the recital's arrangement, etc. to ensure that the audience have a comprehensive listening experience. In this work, we present ragamAI a novel machine learning framework that utilizes the tonic nuances and musical structures in the carnatic music to generate a concert recital that melodically captures the entire range in an octave. Utilizing the underlying idea of playlist and session-based recommender models, the proposed model studies the mathematical structure present in past concerts and recommends relevant items for the playlist/concert. ragamAI ensembles recommendations given by multiple models to learn user idea and past preference of sequences in concerts to extract recommendations. Our experiments on a vast collection of concert show that our model performs 25%-50% better than baseline models. ragamAI's applications are two-fold. 1) it will assist musicians to customize their performance with the necessary variety required to sustain the interest of the audience for the entirety of the concert 2) it will generate carefully curated lists of south Indian classical music so that the listener can discover the wide range of melody that the musical system can offer.

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“…These aspects are even more relevant in distinguishing phrased-based rāgas [3]. Several studies address the limitations of PD-based methods by modeling temporal dynamics of the melody [13][14][15][16][17]. These methods generally use melodic progression templates [15], n-gram distributions [13], or hidden Markov models [16] to capture the sequential information.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies address the limitations of PD-based methods by modeling temporal dynamics of the melody [13][14][15][16][17]. These methods generally use melodic progression templates [15], n-gram distributions [13], or hidden Markov models [16] to capture the sequential information. To the best of our knowledge, there are only two approaches [18] and [14] that explicitly use rāga phrases.…”

Section: Introductionmentioning

confidence: 99%

Phrase-based rĀga recognition using vector space modeling

Gulati

Serrà

Ishwar

et al. 2016

2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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Automatic rāga recognition is one of the fundamental computational tasks in Indian art music. Motivated by the way seasoned listeners identify rāgas, we propose a rāga recognition approach based on melodic phrases. Firstly, we extract melodic patterns from a collection of audio recordings in an unsupervised way. Next, we group similar patterns by exploiting complex networks concepts and techniques. Drawing an analogy to topic modeling in text classification, we then represent audio recordings using a vector space model. Finally, we employ a number of classification strategies to build a predictive model for rāga recognition. To evaluate our approach, we compile a music collection of over 124 hours, comprising 480 recordings and 40 rāgas. We obtain 70% accuracy with the full 40-rāga collection, and up to 92% accuracy with its 10-rāga subset. We show that phrase-based rāga recognition is a successful strategy, on par with the state of the art, and sometimes outperforms it. A by-product of our approach, which arguably is as important as the task of rāga recognition, is the identification of rāga-phrases. These phrases can be used as a dictionary of semanticallymeaningful melodic units for several computational tasks in Indian art music.

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Identifying Ragas in Indian Music

Cited by 40 publications

References 9 publications

Automatic genre classification of Indian Tamil and western music using fractional MFCC

Automatic genre classification of Indian Tamil and western music using fractional MFCC

ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert

Phrase-based rĀga recognition using vector space modeling

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