Shashank Srivastava and Snigdha Chaturvedi
Advisor: Dr. Arnab Bhattacharya, IIT Kanpur
Feature extraction based methods have been used for identifying genres in musical pieces, and there are also attempts to predict the popularity of songs by statistical methods such as clustering. Transportation distances have been extensively used, especially in image searches. Advantages of such methods are their incorporation of the notions of continuity and partial matching. We proposed to combine both methods: we test diﬀerent kinds of feature representations to cluster songs in a database through global level parameters. At the time of search, the input Midi sequence is classiﬁed to one of the clusters by an SVM, and the songs in the sequence with the minimum Levenshtein distances are returned. The sequential approach is seen to yield encouraging results on two standard datasets, for Midi inputs by amateur players.