Content-Based Approach to Automatic Recommendation of Music
This paper presents a content-based approach to music recommendation. For this purpose, a database which contains more than 50000 music excerpts acquired from public repositories was built. Datasets contain tracks of distinct performers within several music genres. All music pieces were converted to mp3 format and then parameterized based on MPEG-7, mel-cepstral and time-related dedicated parameters. All feature vectors are stored as csv files and will be available on-line. A study of the database statistical characteristics was performed. Different splits into train and test sets were investigated to provide the most accurate evaluation of the decision-based solutions. Classification time and memory complexity were also evaluated.
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