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Erschienen in:
2019 | OriginalPaper | Buchkapitel
Intelligent Time of Use Deciding System for a Melody to Provide a Better Listening Experience
verfasst von : M. W. Sohan Janaka, H. U. W. Ratnayake, I. A. Premaratne
Erschienen in: Artificial Intelligence
Verlag: Springer Singapore
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Abstract
Understanding a melody or a song is quite a difficult task for any machine. This research proposes to analyze notations of the music melodies and to decide the best time to play, sing or listen for any given melody by using the knowledge of Hindustani music trained in an Artificial Neural Network. In the proposed system, pre-process module identifies the Aroha, Awaroha, Vadi and Sanwadi Swara of the melody. Those characteristics that are identified from the pre-process module are input to the Artificial Neural Network (ANN). The system uses the expert knowledge of the Hindustani Raagadari music to train the ANN designed and developed using Tensorflow deep learning platform. Training data set for the learning process has been of size 450 whereas testing data set has been 44 from the total of 494 Raaga details. Trained ANN could achieve a testing accuracy of 84%.