Conference Papers

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    Audio segmentation using a priori information in the context of Karnatic Music
    (Institute of Electrical and Electronics Engineers Inc., 2015) Kalyan, V.A.; Sankaranarayanan, S.; Sumam David, S.
    Karnatic Music (KM) is distinct because of the prevalence of gamaka - embellishments to musical notes in the form of frequency traversals. Another important aspect of KM is that the performance style is mostly extempore. Hence, Music Information Retrieval (MIR) tasks in the context of KM are highly challenging. This paper deals with the task of Audio Segmentation and its application to MIR challenges of KM at various levels. This work presents a method that incorporates a priori knowledge about the music system and the audio track at hand for segmenting the audio into its constituent notes. The method uses amplitude and energy based features to train a neural network and an accuracy of 95.2% has been achieved on KM audio samples. The paper also elucidates the application of the method to important MIR tasks such as Music Transcription and Score-Alignment in the context of KM. © 2015 IEEE.
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    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.