Genre classification of songs using neural network

The objective here is to eliminate the manual work of classifying genres of song in each song. With this startup work songs can be classified in real-time and proposed parallel architecture can be implemented on the multi-processing system as well. In this paper a set of features are obtained like b...

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Bibliographic Details
Published in:	2014 International Conference on Computer and Communication Technology (ICCCT) pp. 285 - 289
Main Authors:	Goel, Anshuman, Sheezan, Mohd, Masood, Sarfaraz, Saleem, Aadam
Format:	Conference Proceeding
Language:	English
Published:	IEEE 01-09-2014
Subjects:	Accuracy classification Discrete wavelet transforms echonest Feature extraction genre Mel frequency cepstral coefficient Mood multilayered perceptron Neural networks Rocks songs
Online Access:	Get full text
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Summary:	The objective here is to eliminate the manual work of classifying genres of song in each song. With this startup work songs can be classified in real-time and proposed parallel architecture can be implemented on the multi-processing system as well. In this paper a set of features are obtained like beats/tempo, energy, loudness, speechiness, valence, danceability, acousticness, discrete wavelet transform etc., using Echonest libraries and are fed into the Parallel Multi-Layer Perceptron Network to obtain the genres of the song. The proposed scheme has an accuracy of 85% when used to classify two genres of songs that are Sufi and Classical.
ISBN:	9781479967575 1479967572
DOI:	10.1109/ICCCT.2014.7001506