Music clustering with features from different information sources
IEEE Transactions on Multimedia - Special section on communities and media computing
Adaptive content-based music retrieval system
Multimedia Tools and Applications
Unsupervised tagging of spanish lyrics dataset using clustering
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
| Hi-index | 0.00 |
Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of identifying "similar" artists using both lyrics and acoustic data. In this paper, we present a clustering algorithm that integrates features from both sources to perform bimodal learning. The algorithm is tested on a data set consisting of 570 songs from 53 albums of 41 artists using artist similarity provided by All Music Guide. Experimental results show that the accuracy of artist similarity classifiers can be significantly improved and that artist similarity can be efficiently identified.