Benyamin Matityaho
Miriam Furst
Dept. of Elec. Eng.-Systems, Faculty of Eng., Tel Aviv Univ., Tel Aviv 69978, Israel
Western people who are exposed to different types music, such as pop or classic, can easily distinguish between them after listening to any composition for a few seconds. It is still unclear what the features are in the music that allow people this quick recognition. In the present study a model is proposed that distinguishes between two classes of music, pop and classic. The model is a decision making system that was implemented by integrating outputs of a multilayer neural network (NN). The input nodes to the NN were obtained by a preprocessing algorithm that included the following steps: (1) Dividing the musical composition to intervals of 16.8 ms; (2) applying spectral analysis on each interval; (3) combining the spectral components of T successive intervals into F divisions, which were obtained by dividing the logarithmic of the audiometric frequency range into equal F parts. As a result of the preprocessing algorithm interval of T*16.8 ms of musical signal was presented by F*T input values to the NN. Our results show that it is enough to train the NN with a group of short intervals (i.e., 0.3 s), and to represent its spectrum by about 20 values in order to obtain a 100% success in distinguishing between two types of music.