Identifying Ragas in Indian Music

Vijay Kumar*, Harit Pandya*, C.V. JawaharInternational Institute of Information Technology, Hyderabad, India{vijaykumar.r@research., harit.pandya@research., jawahar@}.iiit.ac.in

AbstractIn this work, we propose a method to identify theragas of an Indian Carnatic music signal. This has several inter-esting applications in digital music indexing, recommendation andretrieval. However, this problem is hard due to (i) the absenceof a fixed frequency for a note (ii) relative scale of notes (iii)oscillations around a note, and (iv) improvisations. In this work,we attempt the raga classification problem in a non-linear SVMframework using a combination of two kernels that representthe similarities of a music signal using two different features-pitch-class profile and n-gram distribution of notes. This differsfrom the previous pitch-class profile based approaches wherethe temporal information of notes is ignored. We evaluated theproposed approach on our own raga dataset and CompMusicdataset and show an improvement of 10.19% by combining theinformation from two features relevant to Indian Carnatic music.

I. INTRODUCTION

Raga - the melodic framework or formalization of melodiesfound in Indian Classical music (Carnatic and Hindustani)composes of a sequence of swaras depicting the mood and sen-timents. Indian music has seven basic swaras (notes) namely(Sa, Ri, Ga, Ma, Pa, Dha, Ni). There are hundreds of ragasin Indian Carnatic music derived from 72 parent or Janakaragas [20], formed by the combination of 12 swarasthanas.Identifying ragas is a central problem for appreciating, com-paring and learning Indian music. Due to the overwhelmingnumber, complicated structure and minor variations of ragas,even humans find it difficult to identify them, without yearsof practice. In this work, we report our initial attempt toautomatically identify the Ragas in Indian Carnatic music.

The identification of ragas is very cognitive, and comesonly after adequate amount of exposure. For automatic iden-tification, some of the characteristics of ragas have to beconverted into appropriate features. This becomes particularlychallenging for Indian music due to the following reasonswhich needs to be addressed while converting a music pieceinto swara strings. (i) A music piece may be composed frommultiple instruments during a performance. (ii) Unlike Westernmusic, the notes in Indian music are not on a absolute scalebut on a relative scale (iii) There is no fixed starting swara in araga. (iv) Notes in Indian music do not have a fixed frequencybut rather band of frequencies (oscillations) around a note. (v)The sequence of swaras in the ragas are not fixed and variousimprovisations are allowed [21] while citing a raga as longas the characteristics of raga are intact. These factors pose aserious challenge for automatic detection of ragas. Also, thesefactors make the problem distinct from applications such asgenre recognition, comparison of vibrato music [22], emotionsrecognition etc., which are usually solved by extracting welldefined features such as MFCC [25], melodic histogram [23]

*Equal contribution

from the main melody or bass line [23] of the music andclassifying using K-NN, naive Bayes [24] or SVM [25] .

Inspite of the above mentioned challenges, there is anunderlying structure found in a raga that can be captured.For example, one can identify a raga by finding its mostprominent swara by counting the number of occurrences orthe duration of each swara [9]. This may give insights into theset of notes and their frequencies in a raga, thereby helping inidentification. Gamakas, the variations of pitch around a notecan be used to identify a raga as only certain type of variationsare allowed in each raga. Characteristic-motifs, similar toPakads in Hindustani music, are the repetitive characteristicsphrases of a raga that provide vital information in identifyinga raga as these characteristic phrases vary from one raga toanother.

In this work, we attempt the raga classification problemusing a non-linear SVM and a combination of two differentkernels. We introduce the kernels to suit the Indian Carnaticmusic that represents the similarities of a raga based on pitch-class profile and n-gram note distribution. This differs fromthe previous pitch-class profile based approaches where thetemporal information of notes is ignored. This approach allowsus to learn a decision boundary in the combined space of Pitch-class profile and n-gram note distribution, where differentragas are linearly separable. Given a music piece, we initiallyextract the predominant pitch values at every instant of time,convert to cents scale, map them to single octave and identifythe stable note regions similar to [2]. These notes are thenused to construct Pitch-class profile and n-gram distribution.While Pitch-class profile represent the distribution of pitchvalues, n-gram distribution provide the information about theoccurrence of short sequence of notes. Thus our approachincorporates the information from both of these features unlikethe previous approaches [2], [4] where either of these featureswere used but not both. We evaluate our approach on anextensive CompMusic dataset [2] consisting of 170 tunescorresponding to 10 ragas and achieve an improvement of10.19% in accuracy.

Related Works: There are some attempts made in identi-fying the raga in a music. One method for raga classificationis through the transcription of raga directly into swaras atevery intervals of time and classifying using a classifier suchas K-NN or SVM. In [6], relative frequencies are usedinstead of using an absolute frequencies as the notes havefixed ratio of frequencies. Though this approach addressesthe issue with scale, it cannot handle multiple instruments.In [7], authors try to rectify this issue by identifying andextracting the fundamental frequency of the singer. All theother frequencies in the scale are then marked down based ontheir respective ratio with the identified fundamental frequencyof the singer. Simple string matching techniques are then

employed to compare ragas. Though this approach performsreasonably well, improvisations and initial transcription errorsmay severely deteriorate the performance.

Few works have focused on a particular characteristic of araga and designing the features accordingly to capture them.For example, authors of [8] compared arohan and avrohan.In [9], Vadi swara is included in their method. Authorsin [4] treat the raga classification similar to word recognitionproblem. They assume the raga compositions to be composedof words formed from the alphabet of notes used in Indianclassical music. They used an Hidden Markov Model (HMM)to learn the transition of notes in different ragas based on twoobservations. First, the sequences of notes for different ragasare well defined in Indian Music and a model based on discretestates with transitions between them possibly captures thesesequences. Second, the notes are small in number requiringa simpler HMM setup. Note that, this method has a largedependency on the initial transcription of the notes. Eventhough this approach incorporates the temporal informationof notes, it ignores the pitch distribution, which also providesvital information in identifying a raga.

Authors of [1] used a Gaussian mixture model (GMM)based HMM using three features, Chroma, MFCC and timbrefeatures. They combined all the three features resulting in a 62dimensional feature vector. They trained on four ragas namelyDarbari, Khamaj, Malhar and Sohini and achieved reasonableperformance. In [2], [10], pitch-class profile distribution is usedto classify a raga. They created 3 variants of the pitch-classprofile based on type of bin weighting and note stabilization.Simple K-NN classifier with KL-divergence distance is used toclassify the ragas. They conducted experiments on extensivedataset of 10 ragas consisting of 170 tunes with at least 10tunes in each raga. Note that, we conduct our experimentson this dataset and achieve superior performance. Also, notethat in the approaches based on pitch-class profiles, temporalinformation of notes is ignored. However, in our approach,we partially capture the temporal information of notes of araga by computing the n-gram histogram. By combining then-gram histogram with pitch-class profiles, performance isfurther improved.

Kernels are used earlier to improve the performance ofmany audio and music related tasks such as classificationand segmentation [16][18] by designing application specifickernels for SVM.

II. KERNELS FOR INDIAN CARNATIC MUSIC

In this section, we describe some of the characteristicfeatures of Indian music and describe how the informationprovided by them can be used to identify a raga.

A. Features of Raga

Authors in [1], [2] characterize the raga by followingcharacteristics:

1) arohana and avarohana: A raga has a fixed ascent(arohana) and descent (avarohana) swaras, withoutany strictness of their sequence in recitation. Thereare certain rules that are essential while citing a raga,though are not strictly followed. Also, many ragas

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