Using Multiple Feature Extraction with Statistical Models to Categorize Music by Genre
In recent years, large capacity portable personal music players have become widespread in their use and popularity. Coupled with the exponentially increasing processing power of personal computers and embedded devices, the way people consume and listen to music is ever changing. To facilitate the categorization of these personal music libraries, a system is employed using MPEG-7 feature vectors as well as Mel-Frequency Cepstral Coefficients classified through multiple trained Hidden Markov Models and other statistical methods. The output of these models is then compared and a genre choice is made based on which model gives the best fit. Results from these tests are analyzed and ways to improve the performance of a genre sorting system are discussed.
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