RAGA IDENTIFICATION: A REVIEW 1KAVITA. M.DESHMUKH, 2P. J. DEORE 1PG Student, Department of Electronics & Telecommunication Engineering, SES’s RCPIT, Shirpur 2HOD, Department of Electronics & Telecommunication Engineering, SES’s RCPIT, Shirpur, E-mail: [email protected], [email protected] Abstract— Ragas are the heart of Indian classical music. Raga is one of the fundamental musical concepts in Indian music. Raga plays an important role in Indian classical music. It is a group of swaras (musical notes) comprising of many features and qualifiers and is described as melodic concept which is led to blossom by the musician. According to characteristics of raga, Indian classical music is further divided into two systems Hindustani (North Indian) classical music, Carnatic (South Indian) classical music. This paper introduces us with some different characteristics for raga identification in Indian classical music. Keywords— Swaras, Raga Identification. I. INTRODUCTION difficult for Indian music due to the following reasons which needs to be addressed while converting a Western music and Indian classical music differed music piece into swara strings. (i) A music piece may from each other with respect to their timing, notes, be composed from multiple instruments during a and different characteristics associated with raga. performance. (ii) Unlike Western music, the notes in Note of western classical music is similar to that of Indian music are not on a absolute scale but on a swaras of Indian classical music. A raga is the unique relative scale (iii) There is no fixed starting swara in a combination of swara, and their substrings. It plays raga. (iv) Notes in Indian music do not have a fixed vital role in an Indian classical music. Indian music frequency but rather band of frequencies has seven basic swaras (notes) namely Sa, Ri, Ga, (oscillations) around a note. (v) The sequence of Ma, Pa, Dha, Ni(Shadja, Rishab, Gandhar, swaras in the ragas are not fixed and various Madhyam, Pancham, Dhaivatand, Nishad).Indian improvisations are allowed [9]. classical music consists of different characteristics associated with particular raga that are not easily II. RELATED WORK extracted or mined by using approach for identification of western music. Raga is a collection There are different endeavors made in distinguishing of different unique notes that are having some special the raga in an Indian music. One technique for raga properties (e.g. arohana, avarohana, pakad, taal, etc.). classification is through the interpretation of raga Raga is divided into two system Hindustani (North straight forwardly into swaras at every intervals of Indian) music, Carnatic (South Indian) music. Both of time and order raga utilizing a classifier, for example these systems differ in their characteristics and K-NN or SVM.In [1], Vijay Kumar, Harit Pandya, performance. C.V. Jawahar investigated the issue of raga Raga is having special characteristics of their timing, recognizable proof in Indian Carnatic music. In light that depending on their nature they can be played in of the perception that, current strategies are either in different timeslots of the day .Notes of a raga light of pitch-class profiles or ngram histogram of arranged in an ascending order forms arohana of that notes yet not both, they attempted to fuse them two in raga and notes of a raga arranged in a descending a multi-class SVM framework by linearly combining order forms avarohana of that raga. Different unique the two kernels. Each of these kernels capture the notes are called swaras in Indian classical music. similarities of a raga based on Pitch-class profiles and Raga identification consists of techniques that ngram histogram of notes.Chordia and Rae [2] identify notes from a piece of music and accordingly described the results of the first large-scale raga classify it into the appropriate raga. Ragas form a recognition experiment. Raga are the central structure very important concept in Hindustani classical music of Indian classical music, each consisting of a unique and capture the mood and emotion of performances set of complex melodic gestures. They have [8]. As a result, automatic raga identification can constructed a system to recognize ragas based on provide a basic information for searching similar pitch-class distributions (PCDs) and pitch-class dyad songs. distribution (PCDDs) calculated directly from the It can also be used by novice musicians who find it audio signal. A large, diverse database consisting of difficult to distinguish ragas which are very similar to 20 hours of recorded performances in 31 different each other and also helpful for the beginners who ragas by 19 different performers was assembled to learn this beautiful art. For automatic identification, train and test the system. Classification was some of the characteristics of ragas have to be performed using support vector machines (SVM). converted into appropriate features. This is very Proceedings of 55th IRF International Conference, 22nd May, 2016, Pune, India, ISBN: 978-93-86083-19-7 94 Raga Identification: A Review Authors of [3] they investigate the problem of scale swaras or symbols in classical music are S(Sa),R (Re independent automatic raga identification by or Ri), G (Ga), M(Ma), P (Pa), D (Dha), N(Ni) .Raga achieving state of the art results using Gaussian is a blend of various swaras that are having some mixture model (GMM) based Hidden Markov Models exceptional properties (e.g. arohana, avarohana, (HMM) and combination of three features consisting Gamakas,Pakad, Taal etc). of chromagram patterns, mel-cepstrum coefficients and timbre features. We also perform the above task A. Arohana and Avarohana using 1) discrete HMMs and 2) classification trees Raga consists of collection of swaras or notes. over swara based features created from chromagrams Depending on collection of notes or swara using the concept of vadi of a raga. They trained on combination and arohana and avarohana, it gives four ragas- darbari, khamaj, malhar and sohini. They identity. Arohana is a collection of raga notes that are have achieved an average accuracy of 97%. [4] arranged in ascending sequence. Avarohana is a Sridhar and Geetha propose a strategy to recognize collection of raga notes that are arranged in the raga of Carnatic music signal. The principle descending sequence. thought process behind Raga identification is that it can be utilized as a decent premise for music data B. Gamakas recovery of Carnatic music melodies or Film songs A note has a specific fixed frequency value. Notes in based on Carnatic music. The input polyphonic music a raga are arranged in a way that there is continuous signal was investigated and made to go through a oscillatory movement about the note, such signal separation algorithm to separate the instrument arrangement of notes is called as gamakas. and the vocal signal. Utilizing their proposed artist identification algorithm they determined the singer C. Pakad with the help of the fundamental frequency of the A Pakad is a characteristic phrase or set of swara singer. The frequency components of the signal were which uniquely identify a raga. For each raga there is then determined and these frequency components, a unique and different Pakad from other raga. Pakad mapped into the swara sequence thereby is a small sequence of swaras in a raga that acts as a consequently deciding the Raga of the specific signature for the raga and an artist often visits and melody. The raga that is coordinated with the raga put revisits the pakad over a performance. It is a major away in the database is the subsequent raga and clue for human raga recognition. For some ragas, the output of the system. Their test data comprises of 30 pakad might be simply the avrohan wrapped over samples/tunes in 3 melakarta ragas sung by 4 artists, arohan and for some others; it might be a totally 175 Talam, raga database having raga name, arohana different pattern of the constituent swaras. avarohana in swara segment structure. Pandey, Mishra, Paul [5] proposed the system'Tansen' D. Tala which depends on Hidden Markov Model and string, Tala refers to a fixed time cycle, set for a particular Pakad coordinating. Their test information comprises composition, which is built from groupings of beats. of result on just 2 ragas Yaman kalian and Bhupali. Talas have cycles of a defined number of beats and They utilized HMM model since notes are little in rarely change within a song. They have specific number and the grouping for raga is exceptionally components, which in combinations can give rise to very much characterized. Baum-Welch learning the variety to exist, allowing different compositions algorithm is utilized for identification of transition to have different rhythms. Carnatic music singers and initial state probability in HMM algorithm. Again usually keep the beat by moving their hands up and to improve execution over HMM, Pakad coordinating down in specified patterns, and using their fingers methodology is utilized by joining learning into the simultaneously to keep time. framework. Prashanth T R, Radhika Venugopala [6] proposed a method for Note Identification in Carnatic CONCLUSION Music from Frequency Spectrum. Instead of using note transcription we can also identify only notes in A brief yet complete prologue to the Raga and its the input song. Their system takes ‘.wav’ files as Identification is introduced. Past Raga input, frequency spectrum characteristics are acknowledgment procedures are overviewed with an analyzed and depending on that they mapped notes. emphasis on their methodology and commitment. Their test data consist of 15 raga alap with 3-8 min Every segment gives distinctive correctnesses and all clip of various artists. They have achieved up to 90% exploration results propose that there is wander scope of accuracy. for development. III. CHARACTERISTICS OF A RAGA REFERENCES Raga consists of sequential arrangement of swaras or [1] Vijay Kumar, Harit Pandya, C.V.