Genetic algorithm based evolution and analysis of music

Abstract

Evolutionary Computing constitutes a category of algorithms that follow biological evolutionary process It works on the basis of trial and error method and progresses to next generation with the best optimized solution Evolutionary Algorithms a subset of Evolutionary Computing algorithms evolve iterative generations Furthermore a subcategory of Evolutionary Algorithms namely Genetic Algorithm simulates the process of natural selection and reproduction Genetic Algorithm can be applied on music to evolve the chords and thereby create evolutionary music that generates music notes automatically Computational methods of music can be categorized into evolution of music and analysis of music Music evolution generates music chords progression or composition based on music notes that iterate in a sequential order Analysis of music involves determining the frequency of musical notes through signal processing methods and thereby classifying them for emotion genre or group of composition of music through classification techniques The main focus of this research is to improvise the existing traditional Genetic Algorithm so as to evolve the best fit music automatically and to analyse music for better classification of raga emotions and genre of songs Evolving music follows the principles of music to generate notes and therefore the generated music complies with the rules of music Both western classical as well as south Indian classical carnatic music follow written symbols or signs to evolve music In western music they are called notes while in carnatic music they are known as swaras The swaras/notes of carnatic music follow a sequential order The swaras of carnatic music S R G M P D N S are respectively equivalent to the notes of western music C D E F G A B C The seven forms of western music notes can be extended up to twelve forms by increasing or doubling the frequency newline