Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music
Applying Natural Language Processing and Deep Learning Techniques for Raga Recognition in Indian Classical Music Deepthi Peri Dissertation submitted to the Faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science in Computer Science and Applications Eli Tilevich, Chair Sang Won Lee Eric Lyon August 6th, 2020 Blacksburg, Virginia Keywords: Raga Recognition, ICM, MIR, Deep Learning Copyright 2020, Deepthi Peri Applying Natural Language Processing and Deep Learning Tech- niques for Raga Recognition in Indian Classical Music Deepthi Peri (ABSTRACT) In Indian Classical Music (ICM), the Raga is a musical piece’s melodic framework. It encom- passes the characteristics of a scale, a mode, and a tune, with none of them fully describing it, rendering the Raga a unique concept in ICM. The Raga provides musicians with a melodic fabric, within which all compositions and improvisations must take place. Identifying and categorizing the Raga is challenging due to its dynamism and complex structure as well as the polyphonic nature of ICM. Hence, Raga recognition—identify the constituent Raga in an audio file—has become an important problem in music informatics with several known prior approaches. Advancing the state of the art in Raga recognition paves the way to improving other Music Information Retrieval tasks in ICM, including transcribing notes automatically, recommending music, and organizing large databases. This thesis presents a novel melodic pattern-based approach to recognizing Ragas by representing this task as a document clas- sification problem, solved by applying a deep learning technique. A digital audio excerpt is hierarchically processed and split into subsequences and gamaka sequences to mimic a textual document structure, so our model can learn the resulting tonal and temporal se- quence patterns using a Recurrent Neural Network.
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