A novel similarity measure: Voronoi audio similarity for genre classification Online publication date: Mon, 20-Nov-2017
by Prafulla Kalapatapu; N.N. Tejas; Siddharth Dalmia; Prakhar Gupta; Bhaswant Inguva; Aruna Malapati
International Journal of Intelligent Systems Technologies and Applications (IJISTA), Vol. 16, No. 4, 2017
Abstract: One of the major challenges in genre classification, recommender systems is to find similarity between the query song and songs in a database. In this paper, we propose a novel similarity measure called Voronoi audio similarity (VAS). We extracted the Content-based features from the audio signal of the song split in frames over a particular time period and we represented each song as a point in 2D space. The proposed system is a two-level classification process, where songs are first clustered by K-means clustering and then a Voronoi diagram is created using centroids from the resulting K-means, which is called the template Voronoi diagram (TVD). This approach learns the decision boundary used for genre classification. The genre of the song could thus be predicted as the genre with the maximum normalised area overlap. Empirical results performed with 10 cross-fold validations on million song subsets of 500 songs showed 78% accuracy.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Intelligent Systems Technologies and Applications (IJISTA):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook