Music data processing and mining in large databases for active media

Abstract

The aim of this paper was to investigate the problem of music data processing and mining in large databases. Tests were performed on a large database that included approximately 30000 audio files divided into 11 classes corresponding to music genres with different cardinalities. Every audio file was described by a 173-element feature vector. To reduce the dimensionality of data the Principal Component Analysis (PCA) with variable value of factors was employed. The tests were conducted in the WEKA application with the use of k-Nearest Neighbors (kNN), Bayesian Network (Net) and Sequential Minimal Optimization (SMO) algorithms. All results were analyzed in terms of the recognition rate and computation time efficiency. © 2014 Springer International Publishing.