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Alapana Generation Using Finite State Machines and Generative Adversarial Networks

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dc.contributor.author Jayatharan, Vithushigan
dc.contributor.author Alwis, Dileeka
dc.date.accessioned 2024-01-16T04:34:02Z
dc.date.available 2024-01-16T04:34:02Z
dc.date.issued 2023
dc.identifier.uri http://repository.kln.ac.lk/handle/123456789/27344
dc.description.abstract The recent advancements in deep learning techniques and computational power have promoted the development of novel approaches for music generation. In this study, generating alapana, an improvisational form of Carnatic music was proposed, by leveraging Generative Adversarial Networks (GANs) and Finite State Machines (FSM). The goal is to create melodious alapana sequences that follow a given input Raga, ensuring continuity and coherence throughout the generated musical piece. The proposed approach incorporates Carnatic music theory rules into the generation process to enhance the structural coherence of the generated alapana. Additionally, various hyperparameter settings were explored to achieve the best performance. The Fréchet Audio Distance, Percentage of Correct Pitches and the Subjective evaluation through human listeners are the evaluation metrics of this approach. The result of this study demonstrates the potential of using GANs and FSM for generating continuous and pleasing alapana sequences in Carnatic music, contributing to the growing body of research in computational music generation. en_US
dc.publisher Jayatharan Vithushigan; Alwis Dileeka (2023), Alapana Generation Using Finite State Machines and Generative Adversarial Networks, International Research Conference on Smart Computing and Systems Engineering (SCSE 2023), Department of Industrial Management, Faculty of Science, University of Kelaniya Sri Lanka. Page 6 en_US
dc.subject generative adversarial network, finite state machine, carnatic music, alapana, raga en_US
dc.title Alapana Generation Using Finite State Machines and Generative Adversarial Networks en_US


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