Faculty Publications
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Item Prediction of aesthetic elements in Karnatic music: A machine learning approach(International Speech Communication Association publication@isca-speech.org 4 Rue des Fauvettes - Lous Tourils Baixas 66390, 2018) Rajan, M.; Vijayakumar, A.; Vijayasenan, D.Gamakas, the embellishments and ornamentations used to enhance musical experience, are defining features of Karnatic Music (KM). The appropriateness of using gamaka is determined by aesthetics and is often developed by musicians with experience. Therefore, understanding and modeling gamaka is a significant bottleneck in applications like music synthesis, automatic accompaniment, etc. in the context of KM. To this end, we propose to learn both the presence and the type of gamaka in a data-driven manner using annotated symbolic music. In particular, we explore the efficacy of three classes of features - note-based, phonetic and structural - and train a Random Forest Classifier to predict the existence and the type of gamaka. The observed accuracy is ∼70% for gamaka detection and ∼60% for gamaka classification. Finally, we present an analysis of the features and find that frequency and duration of the neighbouring notes prove to be the most important features. © 2018 International Speech Communication Association. All rights reserved.Item Predicting Gamakas-The Essential Embellishments in Karnatic Music(Institute of Electrical and Electronics Engineers Inc., 2019) Rajan, M.R.; Vijayasenan, D.; Vijayakumar, A.Gamakas are the musical embellishments used in Karnatic Music. Predicting them from the musical notations plays an important part in applications like automatic synthesis and composition of Karnatic Music. Since there are no well-defined rules governing the use of gamakas, predicting them is a challenging problem. In this work, we propose a method to detect the presence and type of gamakas, in a data-driven manner, from the annotated symbolic music alone. We propose features based on the notes of the song for these tasks. These features are used as inputs to a Random Forest Classifier. We digitise 80 songs from a well known reference book of Karnatic music to create a dataset consisting roughly 30000 notes. We train the classifier on around 12000 notes and test on roughly 18000 notes. From our experiments, the accuracy values obtained for predicting gamaka presence and type are 77% and 70%, respectively. These are significantly better than random classification accuracies. We also analyse the importance of neighbourhood of notes for the detection and classification of gamakas. It is observed that the best accuracy is obtained for gamaka presence detection when a both-sided neighbourhood of size three is considered; and best accuracy for gamaka type prediction is obtained with a both-sided neighbourhood of size one. The analysis performed on the training data reveals that there is information contained in these neighbourhoods for distinguishing between gamaka and non-gamaka notes. © 2013 IEEE.