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A North Indian Raga Recognition Using Ensemble Classifier International Journal of Electrical Engineering and Technology (IJEET) Volume 12, Issue 6, June 2021, pp. 251-258, Article ID: IJEET_12_06_024 Available online at https://iaeme.com/Home/issue/IJEET?Volume=12&Issue=6 ISSN Print: 0976-6545 and ISSN Online: 0976-6553 DOI: 10.34218/IJEET.12.6.2021.024 © IAEME Publication Scopus Indexed A NORTH INDIAN RAGA RECOGNITION USING ENSEMBLE CLASSIFIER Anagha A. Bidkar Research Scholar, Department of Electronics and Telecommunication, Vishwakarma Institute of Information Technology, And Pune Institute of Computer Technology, SPPU- Savitribai Phule Pune University, Pune, Maharashtra, India Rajkumar S. Deshpande Department of Electronics and Telecommunication, JSPM’s Imperial College of Engineering, SPPU- Savitribai Phule Pune University, Pune, Maharashtra, India Yogesh H. Dandawate Department of Electronics and Telecommunication, Vishwakarma Institute of Information Technology, SPPU- Savitribai Phule Pune University, Pune, Maharashtra, India ABSTRACT Indian classical music is an ancient art form. Western and Indian music differ in the sequence of musical notes that are present in the melodic segment. Raga recognition in Indian classical music has been an exciting area of music information retrieval system. This can be useful to create a music library, search raga related music, and music education system. Recognition of raga using machine learning algorithms is a very complex task. This paper aims to find a suitable classifier for a dataset of instrumental music of 12 ragas. The music database has audio files of 4 different musical instruments. For this dataset, the ensemble bagged tree classifier outperforms the raga recognition. This approach suits our dataset to gain accuracy of 96.32%. This paper compares the results with the ensemble subspace KNN model which gives an accuracy of 95.83%. From the derived results, it is observed that ensemble classifiers are better for variants of MFCC features extracted for our North Indian Raga Dataset. Key words: North Indian Raga, Audio Feature Extraction, (MFCC) Mel Frequency Cepstral Coefficients, Ensembel Bagged Tree, Ensemble subspace KNN Cite this Article: Anagha A. Bidkar, Rajkumar S. Deshpande, Yogesh H. Dandawate, A North Indian Raga Recognition using Ensemble Classifier, International Journal of Electrical Engineering and Technology (IJEET), 12(6), 2021, pp. 251-258. https://iaeme.com/Home/issue/IJEET?Volume=12&Issue=6 https://iaeme.com/Home/journal/IJEET 251 [email protected] A North Indian Raga Recognition using Ensemble Classifier 1. INTRODUCTION Content-based music information retrieval system is the ongoing current research area. Developing the system for Indian classical music recognition is a challenging task. Indian classical music has two basic forms such as North Indian or Hindustani and South Indian or Carnatic music. This paper presents the work done on recognition of North Indian raga music generated by Indian musical instruments such as sitar, sarod, santoor and flute. Indian music is based on the concept of the raga. A raga has a specific combination of note-sequence described in music literature. In concert, the performer can improvise the raga segments as per his mood by considering the rules of the raga in mind. That has a spiritual impact on the performer and listener also. Due to this bizarre nature, it is a challenging task to analyse these music signals and make the machine learn and recognize the raga. All raga is characterized by several attributes like aroh means ascending sequence of notes; avroh means descending sequence of notes; vaadi is a prominent frequent repetition of note, samvadi is the second prominent repeating note and pakad has a specific set of notes which indicate raga; besides the sequence of notes. Two performances of the same person and another person may not be the same, there can be some variations, not identical. The study of musical ragas is carried out using music theory by extracting acoustic features of musical raga segments. The approach to recognising ragas in Indian classical music is presented in novel ways using two classifiers, and the results are promising. The rest of the paper is organized as follows. Section 2 highlights the literature review. Explanation of the proposed method is mentioned in section 3. In section 4 experimental results are presented. Finally, the conclusion and future scope are discussed in section 5. 2. LITERATURE REVIEW This section discusses various approaches and methods for identifying ragas in Indian classical music. Bhat et al. [1] compared classification models for 15 ragas of carnatic instrumental audio and achieved a 97% accuracy rate. As features, the researchers used spectral centroid, spectral bandwidth, spectral roll off, chroma features, and Mel Frequency Cepstral Coefficients (MFCC). Classifiers such as artificial neural network, XGboost, convolutional neural network, and Bidirectional long short-term memory process the features. The team mentioned that machine learning classifiers outperform deep learning models. Furthermore, the work can be expanded to include Hindustani classical music and a larger number of ragas. Kumar et al. [2] used 120 hours carnatic music dataset. Time delayed melody surface will extract melody tonal features and using a k-nearest neighbour with variations of distance measurements are analyzed. According to K. Pravin Kumar et al. [3], the 72 Melkarta Caranatic ragas are assigned a group label, and classification on the class-labeled data is performed using clustering. The J48 decision tree and PART (Projective Adaptive Resonance Theory) rule-based classifier correctly classified the groups with 94.4% accuracy. The Multilayer Perceptron gives 93.5% accuracy. The JRIP (Java-based Repeated Incremental Pruning to Produce Error Reduction) algorithm demonstrated 91.6% accuracy. The accuracy of the Nave Bayes and Random Forest algorithms was 90.2 %. K nearest Neighbor classifier gives 83.3 %. Anand [4] conducted experiments on Carnatic Comp-music datasets containing five and eleven ragas to develop a convolutional neural network (CNN) capable of learning the distinguishing characteristics of a raga from the predominant pitch values of a song. The model's accuracy was 96.7% and 85.6 %, respectively. The model has been tested for allied ragas and found to be 54% accurate. Sarkar et al. [5] experimented with 23 different ragas. There are a total of 1648 raga clips in the dataset. Each clip is 45 seconds long. There are 1190 raga clips from instrumental audio https://iaeme.com/Home/journal/IJEET 252 [email protected] Anagha A. Bidkar, Rajkumar S. Deshpande, Yogesh H. Dandawate and 458 raga clips from vocal performances among them. For each audio signal, a pitch-based swara (note) profile is created, which generates a histogram of the dominant swaras as well as the energy distribution of the swaras. The accuracy achieved with the SVM classifier for the Instrumental dataset and the vocal dataset was 84.79% and 70.52%, respectively. When domain knowledge is used, classification errors can be reduced. Anoop [6] and team found 32 ragas spectrogram of flute samples. 95% accuracy is gained by phrase matching. Anitha and Gunavathi [7] selected musical features extracted from MIRTOOLBOX using Neutrosophic Cognitive Maps (NCMs). Carantic 72 melkarta raga classification is attempted with a gaussian kernel Support Vector Machine (SVM) and achieves 96 % accuracy. Alekh [8] used the GTraagDB database, which contains 127 samples from 31 different ragas. Raga pitch movements and tonal extraction were performed. The Neural Network classifier with the Bhattacharya Distance is used for raga recognition and tonic estimation, with a kernel density pitch distribution through 5-cent granularity. In the case of tonic estimation, the minimum error rate for 15-cent precision was calculated to be 4:92%. The same configuration produced the lowest error rate 8.5% for raga estimation. Rajani [9] attempts to identify a Carnatic raga based on its octave-folded prescriptive notations. They limit the notations to seven notes and map the finer note position information. A dictionary-based approach captures the statistics of raga notation's repetitive note patterns. The proposed stochastic models of repetitive note patterns were obtained from raga notations of known compositions and achieved a 96% accuracy. Most previous approaches to the problem of raga identification have relied heavily on explicitly developing features that can capture the various characteristic features of a raga. Raga detection can be accomplished by computing raga similarity using a distance measure or by using a classifier on those features. There has been a lot of work done on Carnatic raga recognition, but there is still a lot of work to be done on Hindustani or North Indian raga recognition. In this paper, researcher attempted to recognize North Indian classical ragas by reducing the size of the feature set and finding an appropriate classifier to identify the raga. 3. PROPOSED METHOD Fig.1 shows the proposed methodology. An ensemble classifier model is implemented to get the raga output. The generated database is divided into training and testing dataset. Features are extracted after preprocessing of the database. Ensemble classifier model is implemented after data analysis of features and raga output is generated. Details are given in subsequent sections. 20 sec .wav Pre- Feature Training Set processing Extraction Ensemble Classifier Raga Output Model 20 sec .wav Pre- Feature Testing
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