SanS: Classification of Sanskrit Mantras using Speech Processing

dc.contributor.authorKeerthan Kumar, T.G.
dc.contributor.authorUdaya, S.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2026-02-06T06:33:36Z
dc.date.issued2024
dc.description.abstractChandas is a classification metric used in Sanskrit Mantras. They are essentially meant to maintain a form of rhythm for each and every mantra, which gives the hymns their distinctive chanting pattern. Sanskrit hymns (mantras) have many different classifications. Often, before chanting, they are invoked as such in the order of Rishi (author of the hymn), Chanda (rhythm), Devata (god to which invoked), and Viniyoga (use of such hymn). One such example is the Gayatri hymn, which is from Gayatri Chanda. Other examples are verses of the Bhagavad Gita, which are entirely in Anushtup Chanda. Chandas is an essential component of Sanskrit Mantras and forms an integral part of it. Knowledge of Chandas is essential for the study of Sanskrit Poetry, and without knowing what a Chanda is, one cannot analyze Sanskrit hymns. Essentially, Chandas are the meters used to keep track of the mantras. Chandas formulate the rhythm of the mantra and the way that it should be chanted. Based on the number of syllables, there are seven different Chandas, and mantras are usually classified into one of these seven. Identification of Chandas is usually specified in the beginning before chanting; however, in cases where the Chandas are not specified, one may have to do it manually, which may be cumbersome, especially for Chandas with many syllables. In this work, we propose a novel approach called Classification of Sanskrit Mantras using Speech Processing Technology (SanS) to determine the Chanda of a mantra, given the audio file of a Sanskrit mantra and using a Wavenet architecture. The proposed SanS predicts the type of Chanda by counting the number of syllables, giving 81.57% accuracy compared to other works. © 2024 Copyright held by the owner/author(s).
dc.identifier.citationACM International Conference Proceeding Series, 2024, Vol., , p. 427-431
dc.identifier.urihttps://doi.org/10.1145/3675888.3676086
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/28746
dc.publisherAssociation for Computing Machinery
dc.subjectAudio Processing
dc.subjectChandas
dc.subjectDeep Learning
dc.subjectSanskrit Mantras
dc.subjectSyllables
dc.titleSanS: Classification of Sanskrit Mantras using Speech Processing

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