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 data-base that included approximately 30000 audio files divided into 11 classes cor-responding to music genres with different cardinalities. Every audio file was de-scribed by a 173-element feature vector. To reduce the dimensionality of data the Principal Component Analysis (PCA) with variable value of factors was em-ployed. The tests were conducted in the WEKA application with the use of k-Nearest Neighbors (kNN), Bayesian Network (Net) and Sequential Minimal Op-timization (SMO) algorithms. All results were analyzed in terms of the recogni-tion rate and computation time efficiency.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-319-09912-5_8
- Category
- Aktywność konferencyjna
- Type
- materiały konferencyjne indeksowane w Web of Science
- Language
- angielski
- Publication year
- 2014
