Realtime Portable Music's Genre Classificator with The Kohonen (SOM) Methods Using Raspberry PI

Abstract

Music genre is one of the digital music data that is determined to classify music based on all the character
equations of each type. The characteristics in question are usually seen from the frequency of music, rhythmic
structure, instrumentation structure, and harmony content that the music has. Classification of music genres in
realtime (automatic / not manual), giving effect to the classification is no longer relative / subjective, because it
is done based on predetermined parameters. In this study Raspberry Pi microcomputer is used, which is quite
concisely used as a portable media and is quite powerful for realtime data processing. Raspberry Pi is used as a
sound processing unit, music genre identifier, and information on the results of the introduction of the music
genre. This system input is in the form of music sound (realtime), while the system output is information (text)
about the music genre. Whereas for the process of recognizing the music genre, the Self Organizing Maps (SOM)
type Neural Neural Network (JOM) method is also used, or also known as the Kohonen ANN Network. The
feature extraction stage uses the Music Genre Recognition by Analysis of Text (MUGRAT) method, with nine
features related to the spectral surface of music, and six features related to beat / rhythm of music. Mel
Frequency Cepstral Coefficients (MFCCs) feature extraction process was carried out as input from the
classification process using the Self Organizing Map (SOM) method. The classification results using the SOM
method give an accuracy value of 74.75%. Accuracy of classification results using training data as many as 400
pieces which are divided into 4 musical genres amounting to 74.75%.