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Music Recommendation System

Hoffmann P., Kaczmarek A., Spaleniak P., Kostek B.

Journal of Telecommunications and Information Technology

2014

nr 2

59--69

The paper focuses on optimization vector content feature for the music recommendation system. For the purpose of experiments a database is created consisting of excerpts of music files. They are assigned to 22 classes corresponding to different music genres. Various feature vectors based on low-level signal descriptors are tested and then optimized using correlation analysis and Principal Component Analysis (PCA). Results of the experiments are shown for the variety of feature vectors. Also, a music recommendation system is presented along with its main user interfaces.

Music Recommendation Based on Multidimensional Description and Similarity Measures

Kostek B., Kaczmarek A.

Fundamenta Informaticae

2013

Vol. 127, nr 1-4

325--340

This study aims to create an algorithm for assessing the degree to which songs belong to genres defined a priori. Such an algorithm is not aimed at providing unambiguous classification-labelling of songs, but at producing a multidimensional description encompassing all of the defined genres. The algorithm utilized data derived from the most relevant examples belonging to a particular genre of music. For this condition to be met, data must be appropriately selected. It is based on the fuzzy logic principles, which will be addressed further. The paper describes all steps of experiments along with examples of analyses and results obtained.