Music pattern mining for chromosome representation in evolutionary composition
Chien‐Hung Liu, Chuan-Kang Ting · 2015
Artificial intelligence (AI) has bloomed in many novel fields such as computational creativity. Recently, research on automatic composition using AI technology, especially evolutionary algorithms, has received considerable promising results. Traditionally, chromosomes are represented as a series of numbers to indicate the notes for evolutionary composition. This study attempts to explore the composition styles by mining music patterns of a specific composer. The patterns are used as genes for chromosome representation. Accordingly, the composition styles are considered in generating music by evolutionary algorithms. The fitness function is based on music theory to smooth the progression between phrases. Experimental results show that the patterns mined from compositions can reflect the composer's style and benefit generating satisfactory songs by evolutionary algorithms.