Classification of Pop and RnB (Rhythm and Blues) Songs with MFCC Feature Extraction and K-NN Classifier

Classification is a technique for designing functions based on observations of attributes in a data so that data can be mapped that do not have a class which in this study can be called genres, into data that has been classified according to the given rules. In this research, music classification is conducted to determine whether the class or genre of music is pop or RnB (Rhythm and Blues) by using MFCC as the feature extraction method and K-NN as the classification method. The test results in this study obtained an accuracy of 77.5% with an optimal value of k = 31 as a parameter in K-NN.

Sound + Bodies in Community = Music

The analytical framework of sound studies is transforming our understanding of the political force of music. Following the lead of scholars like Nina Eidsheim and Salomé Voegelin, this essay considers the resonating force of listening bodies as a central factor in the musical construction of political community. This essay traces the tradition of African American music from congregational gospel singing through early rhythm and blues up to the twenty-first-century rap of Kendrick Lamar, showing how particular musical techniques engage the bodies in the room, allowing communities of difference to find their rhythms together.