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 characterequations of each type. The characteristics in question are usually seen from the frequency of music, rhythmicstructure, instrumentation structure, and harmony content that the music has. Classification of music genres inrealtime (automatic / not manual), giving effect to the classification is no longer relative / subjective, because itis done based on predetermined parameters. In this study Raspberry Pi microcomputer is used, which is quiteconcisely used as a portable media and is quite powerful for realtime data processing. Raspberry Pi is used as asound processing unit, music genre identifier, and information on the results of the introduction of the musicgenre. 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. Thefeature extraction stage uses the Music Genre Recognition by Analysis of Text (MUGRAT) method, with ninefeatures related to the spectral surface of music, and six features related to beat / rhythm of music. MelFrequency Cepstral Coefficients (MFCCs) feature extraction process was carried out as input from theclassification process using the Self Organizing Map (SOM) method. The classification results using the SOMmethod give an accuracy value of 74.75%. Accuracy of classification results using training data as many as 400pieces which are divided into 4 musical genres amounting to 74.75%.