Recognizing Music Features Pattern Using Modified Negative Selection Algorithm for Songs Genre Classification

摘要

Previous studies have proven that imitating the mechanism of recognizing alien cells is beneficial and provides so many solutions to the pattern recognition related problems. These efforts emulate the human immune system in recognizing the cells by considering every essential component or features of the subjects. In this research, the focus is on analyzing the music features patterns to recognize various songs genres by emphasizing the features from the artists' voices, the melody of the music and even the sounds of the musical instruments used. Three fundamental music contents are investigated which are timbre, rhythm, and pitch based features. The main objective of this research is to recognize the music features from different genres using the modified negative selection algorithm fundamental procedures that are the censoring and monitoring modules. The results of the experimental works are remarkable and are comparable to previous works in the music recognition and classification works. In this highlight, stages of music recognition are emphasized where feature extraction, feature selection, and feature classification processes are explained. Comparison of performances between proposed algorithm and other classification technique are discussed.