Below you can read the papers of the past Conferences
New Music Concepts 2016 (Hong Kong)
ISBN:
978-88-6551-221-0
Author(s):
Sahar Arshi, Darryl N. Davis
Abstract:
The research described here reflects our initial experiments in producing synthetic traditional Persian Music. Liquid Persian Music (LPM) is an assisting software capable of producing audio output. Equipped with cellular automata (CA) as a creative computational intelligence model and pattern recognition rules, it can produce novel musical material. This enables the exploration of new dimensions of music. Navigating in the LPM musical search space can be considered as a problem suitable for evolutionary algorithms. To help constraining the search space to desired musical classes, a fitness function based on a machine learning tool has been introduced to the problem. The fitness function is a support vector machine trained with features extracted from Persian music and LPM random sequences. The features are based on Zipf’s law proportions as one of the measurable universals for characterizing the aesthetical aspects occurring in natural phenomena. The search is guided towards following the power law proportions from Persian music during the GA evolution. The details of this approach are presented in the paper. The article concludes with an overview of the future work.
Keywords:
audio software, machine learning, synthetic traditional Persian Music