Modeling and Analysis of Indian Carnatic Music Using Category Theory

Padi, Sarala; Breiner, Spencer; Subrahmanian, Eswaran; Sriram, Ram D.

doi:10.1109/tsmc.2016.2631130

Cited by 4 publications

References 33 publications

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Modeling Cognitive System with Applied Machine learning in Additive Manufacturing using Fifth Generation Computer Systems

Ajithbabu

Krishnaranjani

Rohith

et al. 2021

J. Phys.: Conf. Ser.

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Data mining is known as data, which promotes the growth of knowledge discovery. It is the process of analyzing descriptive data from divergent perspectives and summarizing it into valuable information, which is high-level music processing out of which a machine intends to decipher the Raaga of a frequency or the pitch of the music. One of the ways to approach the task is by comparing selected music features from the spectrum and a Raaga database. Recognizing emotion from music has become one of the active research themes in image processing and applications based on human-computer interaction. This research conducts an experimental study on recognizing facial emotions. The flow of the emotion recognition system includes the basic process in the singular value decomposition system. These include music acquisition, pre-processing of a spectrum, feature detection, feature extraction, classification, and when the emotions are classified, the system assigns the particular user music according to his emotion. The proposed system focuses on live images taken from the music database. This research aims to develop an automatic music recognition system for innovative manufacturing through the additive manufacturing route. The emotions considered for the experiments include happiness, Sadness, Surprise, Fear, Disgust, and Anger that are universally accepted. This paper overviews the progress of applying Additive manufacturing in Applied Machine learning which sustains the capability of disruptive digital manufacturing.

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