Automatic genre classification of music content
Automatic genre classification of music content
Scaringella, Nicolas and Zoia, Giorgio and Mlynek, Daniel
IEEE Signal Processing Magazine 2006
Abstract : This paper presents a novel approach to the task of automatic music genre classification which is based on ensemble learning. Feature vectors are extracted from three 30-second music segments from the beginning, middle and end of each music piece. Individual łdots
