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Classification of Music Genre using Neural Networks with Cross-Entropy Optimization and Soft-Max Output

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International Journal of Computer Applications
© 2015 by IJCA Journal
Volume 119 - Number 12
Year of Publication: 2015
Authors:
Dharin Shah
Chirag Sachdev
Bhavik Shah
10.5120/21123-4013

Dharin Shah, Chirag Sachdev and Bhavik Shah. Article: Classification of Music Genre using Neural Networks with Cross-Entropy Optimization and Soft-Max Output. International Journal of Computer Applications 119(12):33-38, June 2015. Full text available. BibTeX

@article{key:article,
	author = {Dharin Shah and Chirag Sachdev and Bhavik Shah},
	title = {Article: Classification of Music Genre using Neural Networks with Cross-Entropy Optimization and Soft-Max Output},
	journal = {International Journal of Computer Applications},
	year = {2015},
	volume = {119},
	number = {12},
	pages = {33-38},
	month = {June},
	note = {Full text available}
}

Abstract

In this paper, an abstract model to predict the genre of a music audio file is proposed (specifically a wave file). The output of the model is the probability distribution along the considered genres. A machine learning approach is employed. The adaptive learning process is modeled by neural networks with back-propagation as its learning algorithm and cross entropy as its optimization function. The emphasis is on feature extractors since the learning paradigm is well known to other applications. Simple Analysis on the Features were performed for appropriate selection.

References

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