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Music Genre Classification using Improved Artificial Neural Network with Fixed Size Momentum
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 101 - Number 14
Year of Publication: 2014
|
10.5120/17756-8857 |
Nimesh Prabhu, Ashvek Asnodkar and Rohan Kenkre. Article: Music Genre Classification using Improved Artificial Neural Network with Fixed Size Momentum. International Journal of Computer Applications 101(14):25-29, September 2014. Full text available. BibTeX
@article{key:article,
author = {Nimesh Prabhu and Ashvek Asnodkar and Rohan Kenkre},
title = {Article: Music Genre Classification using Improved Artificial Neural Network with Fixed Size Momentum},
journal = {International Journal of Computer Applications},
year = {2014},
volume = {101},
number = {14},
pages = {25-29},
month = {September},
note = {Full text available}
}
Abstract
Musical genres are defined as categorical labels that auditors use to characterize pieces of music sample. A musical genre can be characterized by a set of common perceptive parameters. An automatic genre classification would actually be very helpful to replace or complete human genre annotation, which is actually used. Neural networks have found overwhelming success in the area of pattern recognition. The standard back propagation algorithm is used for training network with fixed learning rate. This paper classifies music into genres using improved neural network with fixed size momentum. Finally we validate the proposed algorithm with experimental results of accuracy.
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