A COMPARATIVE STUDY OF INDIAN AND WESTERN MUSIC FORMS Parul Agarwal1, Harish Karnick2 Bhiksha Raj Indian Institute of Technology, Kanpur, India Carnegie Mellon University, USA [email protected] [email protected] [email protected] ABSTRACT multiple genres. For example Hindustani music has Dhru- pad, Khayal, Tarana as classical genres and Thumri, Dadra, Music in India has very ancient roots. Indian classical Tappa, Bhajan as semi-classical genres - amongst others. music is considered to be one of the oldest musical tra- Popular music has multiple folk genres based on region, ditions in the world but compared to Western music very film music and adhunik or modern music which is influ- little work has been done in the areas of genre recognition, enced by multiple Indian and western genres. classification, automatic tagging, comparative studies etc. At the base of classical and semi-classical Indian mu- In this work, we investigate the structural differences be- sic is a raga(s) which is described as a mood or senti- tween Indian and Western music forms and compare the ment expressed by a microtonal scale form [28]. A raga two forms of music in terms of harmony, rhythm, micro- can be sung in many Indian genres like Dhrupad, Khayal, tones, timbre and other spectral features. To capture the Thumri etc with its corresponding microtonal scale based temporal and static structure of the spectrogram, we form on a natural harmonic series. Popular Indian music, on a set of global and local frame-wise features for 5- genres the other hand, is not necessarily based on ragas and can of each music form. We then apply Adaboost classification have tonal elements that can differ from the traditionally and GMM based Hidden Markov Models for four types of accepted classical norms. Almost all Indian music is based feature sets and observe that Indian Music performs bet- on melody - single notes played in a given order. Western ter as compared to Western Music. We have achieved a music has strong harmonic content i.e. a group of notes best accuracy of 98.0% and 77.5% for Indian and Western called chords played simultaneously. Unlike Indian tonal musical genres respectively. Our comparative analysis in- system, the Western tonal system is divided into twelve dicates that features that work well with one form of music equal intervals. Each Indian genre has its own well-defined may not necessarily perform well with the other form. The structure which makes it different from other forms of mu- results obtained on Indian Music Genres are better than the sic. To capture the inherent structure of Indian music and previous state-of-the-art. use it for genre classification is a challenging problem. Through our work we try to explore this area and focus on the following research questions: 1. INTRODUCTION Due to technology advances the size of digital music col- • What are the structural differences between Indian lections have increased making it difficult to navigate such and Western musical forms? collections. Music content is very often described by its genre [1], though the definition of genre may differ based • How well can the audio features used for genre clas- on culture, musicians and other factors. Considerable anal- sification in Western music, capture the characteris- ysis has been done on western music for genre classifica- tics of Indian genres? tion [2] [3] and content analysis [4]. Although, Indian music, especially classical music, is • Is there a feature set that can be used for cross-cultural considered to be one of the oldest musical traditions in music genre classification? In this case, for Indian the world, not much computational analytical work has and Western music. been done in this area. Indian music can be divided into two broad categories classical and popular. Classical mu- 2. RELEVANT WORK sic has two main variants Hindustani classical prevalent largely in north and central India and Carnatic classical 2.1 Study on Genre classification by humans prevalent largely in the south of India. Each variety has Research has been done on the human ability to classify music genres. Perrot et al. [16](1999) reported that hu- Permission to make digital or hard copies of all or part of this work for mans with little musical training were able to classify gen- personal or classroom use is granted without fee provided that copies are res with an accuracy of about 72% on a ten-genre dataset of not made or distributed for profit or commercial advantage and that copies a music company, based on only 300 milliseconds of audio. bear this notice and the full citation on the first page. Soltau (1997) conducted a Turing-test in which he trained c 2013 International Society for Music Information Retrieval. 37 people to distinguish rock from pop music and found that his automatic music classifier was similar to their per- 2.4 Contributions of the paper formance on the same dataset. Lippens et al. [15](2004) There is no study, to the best of our knowledge, that com- conducted an experiment with six genres and observed that pares classification of Indian musical genres with West- people were able to classify with an accuracy of around ern musical genres. Our work makes the following con- 90%. Considerable work has been done in the area of tributions: first, this paper introduces a new research prob- automatic music genre classification and with increasing lem on cross-cultural music genre classification by doing a improvement in classification methods there is a distinct comparative study on Indian and Western musical forms in possibility that they may outperform humans in the near terms of their structure and characteristics. Second, while future. there are many standard datasets on genre classification available for Western music like GTZAN dataset [5], 2005 2.2 Genre classification of Western Music MIREX dataset, Magnatune [21], USPOP [22] etc. There is no standard dataset for Indian genre classification. For Many supervised and unsupervised methods have been used our study, we built a dataset of 5 Indian genres- Dhru- to automatically classify recordings into genres. Commonly pad, Thumri, Carnatic, Punjabi and Ghazals. There are used supervised classification methods are k-nearest Neigh- 100 samples in each genre where each sample is an audio bours (KNN) [5] [17], Support Vector Machines (SVMs) clip of 30 seconds length. This dataset can be used for [19] [17], Gaussian Mixture Models (GMMs) [5] [20] [17], future work on Indian music genre classification. Third, Linear Discriminant Analysis (LDA) [17] [18], non-negative we have incorporated short-time and long-time features to Matrix Factorization (NMF) [2], Artificial Neural Networks capture the static and temporal structure of the spectro- (ANNs) and Hidden Markov Models (HMMs). In the un- gram and have analysed both cultural forms. Fourth, we supervised domain, there is work using the k-means ap- propose a novel approach of using timbre and chromagram proach and hierarchical clustering. features with Gaussian Mixture Models(GMM) based Hid- To the best of our knowledge, there is no work in the den Markov Models to identify the patterns in Indian music literature that analyses which features are best for music which gave an overall accuracy of 98.0%, which is better genre classification. There is work using Fourier analy- than the previous state-of-the-art [12] which also worked sis, cepstral analysis and wavelet analysis [17] [24]. Li with 5-genres. et al. [17] and J. Bergstra et al. [3] worked with a set of parameters like spectral centroid, spectral rolloff, spectral 2.5 Outline of Paper flux, zero crossings and low energy. Y.Panagakis et al. [25] used sparse representation-based classifiers on audio tem- In section 3 we discuss the dataset used for the experiments poral modulations of songs for genre classification. Sala- and the structure and characteristics that make an Indian mon et al. [26] used a combination of low level features genre distinct from other genres. Sections 4 and 5 give a with high level melodic features derived from pitch con- detailed explanation of the features chosen and classifiers tours and showed that the combination outperforms the re- used. Section 6 discusses the experimental results. Section sults from using only low level features. 7 outlines possible future work. Tzanetakis et al. [5] proposed to classify musical genres using K -Nearest Neighbours and GMMs based on timbral 3. DATASET texture and rhythmic features. E. Benetos et al. [2] used In this section, we look at the structural differences be- non-negative tensor factorization (NTF) with GTZAN data tween Indian and Western music forms and discuss the set [5]. J. Bergstra et al. [3] used GMM based Adaboost characteristic features of the Indian genres used in the ex- classification with aggregate features dividing frames into periment. segments. Hidden Markov Models were used by T. Lan- glois et al. [10] for genre classification based on timbre 3.1 Indian Music information. For the experiments we have considered five popular gen- res - Hindustani (Dhrupad + Thumri), Carnatic, Folk (Pun- 2.3 Genre classification of Indian Music jabi) and Ghazal. We constructed our own dataset because Compared to western music very little work has been done no standard dataset is available. Each genre contains 100 in Indian musical genre classification. S.Jothilakshmi et audio recordings each 30 seconds long extracted randomly al. [12] used kNN and GMM on spectral and perceptual from recordings available on-line. All samples are vocals. features and achieved an accuracy of 91.23% with 5 In- 3.1.1 Indian classical Music dian genres- Hindustani, Carnatic, Ghazal, Folk and Indian western. P. Chordia et al. [13] used pitch profiles and Pitch- Indian classical music is based on melody without the pres- Class Distributions for raga identification.