Evolution of Musical Organisms

The development of software for musical applications has led to a proliferation of elaborate control paradigms with extremely large parameter spaces. These spaces can be daunting to explore interactively because of their vastness. Furthermore, parameters often interact in ways not made explicit by the control panel, effectively increasing the complexity of the space even further. Application of genetic algorithms (GAs) can be used to search through these vast spaces in a highly efficient manner. Coordinated control of interacting parameters is handled automatically by this system. Even for control paradigms that are well understood, the genetic algorithm can efficiently search out control settings that would have been otherwise unlikely to arise. The author has developed a software system that employs genetic algorithms to evolve \u27musical organisms\u27. The system is built on MidiForth, the author\u27s computer-assisted composition software [Degazio 1988, 1993] and employs many unique functions developed in previous research. This paper describes the second phase of research; future work will extend the GA searching technique to abstract, subjective musical concepts such as density and smoothness

A computational framework for aesthetical navigation in musical search space

Paper presented at 3rd AISB symposium on computational creativity, AISB 2016, 4-6th April, Sheffield. Abstract. This article addresses aspects of an ongoing project in the generation of artificial Persian (-like) music. Liquid Persian Music software (LPM) is a cellular automata based audio generator. In this paper LPM is discussed from the view point of future potentials of algorithmic composition and creativity. Liquid Persian Music is a creative tool, enabling exploration of emergent audio through new dimensions of music composition. Various configurations of the system produce different voices which resemble musical motives in many respects. Aesthetical measurements are determined by Zipf’s law in an evolutionary environment. Arranging these voices together for producing a musical corpus can be considered as a search problem in the LPM outputs space of musical possibilities. On this account, the issues toward defining the search space for LPM is studied throughout this paper

A novel approach to automatic music composing: Using genetic algorithm

Artificial music composition is one of the ever rising problems of computer science. Genetic Algorithm has been one of the most useful means in our hands to solve optimization problems. By use of precise assumptions and adequate fitness function it is possible to change the music composing into an optimization problem. This paper proposes a new genetic algorithm for composing music. Considering entropy of the notes distribution as a factor of fitness function and developing mutation and crossover functions based on harmonic rules and trying to keep the melodies intact during these processes would result in a musical piece pleasant to human ears and interesting for human mind. This algorithm does not have the constraints of the previous algorithms. Restraining mutation and crossover functions with a goal of producing melodies based on acceptable melodies composed by humans, this algorithm is not bound to any genre, instrument or melody. The experimental results of this approach show that it is near to the human composing and the results produced from it are more acceptable than the ones produced by its predecessors

Melody Generation using an Interactive Evolutionary Algorithm

Music generation with the aid of computers has been recently grabbed the attention of many scientists in the area of artificial intelligence. Deep learning techniques have evolved sequence production methods for this purpose. Yet, a challenging problem is how to evaluate generated music by a machine. In this paper, a methodology has been developed based upon an interactive evolutionary optimization method, with which the scoring of the generated melodies is primarily performed by human expertise, during the training. This music quality scoring is modeled using a Bi-LSTM recurrent neural network. Moreover, the innovative generated melody through a Genetic algorithm will then be evaluated using this Bi-LSTM network. The results of this mechanism clearly show that the proposed method is able to create pleasurable melodies with desired styles and pieces. This method is also quite fast, compared to the state-of-the-art data-oriented evolutionary systems.Comment: 5 pages, 4 images, submitted to MEDPRAI2019 conferenc

Variable 4: A Dynamical Composition for Weather Systems

Variable 4 is a multichannel sound installation that uses meteorological sensors and a multi-layered array of algorithmic processes to transform weather data into musical patterns in real time. This paper describes the work in detail, outlining its historical context, systems infrastructure and installation specifics. The piece is discussed in relation to sonification and environmental installation, and observations are made on the process of siting a complex sound work in the natural world

Algorithmic music composition: a survey

This paper surveys some of the methods used for algorithmic composition and their evolution during the last decades. Algorithmic composition was motivated by the natural need to assist and to develop the process of music creation. Techniques and applications of algorithmic composition are broad spectrum, ranging from methods that produce entire works with no human intervention, up to methods were both composer and computer work closely together in real-time. Common algorithms used for music composition are based in stochastic, deterministic, chaotic and artificial intelligence methods.N/

Evolutionary perspectives in computer music

This paper presents a brief overview of music evolution - western and non-western music - from its genesis to serialism and the Darmstadt school. Some mathematical aspects of music are then presented and confronted with music as a form of art. Some questions follow: are these two (very) distinct aspects compatible? Can computers be of real help in automatic composition? Evolutionaty Algorithms (EAs) - Genetic Algorithms (GAs), Genetic Programming (GP), Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) - are then introduced and some results of GAs and GPs application to music generation are analysed. Variable fitness functions and PSO application seems a promising way to explore. However, what output should be envisaged? Should we expect that computer music sounds as human music, or should we look for a totally different way to explore and listen? How far can go computer creativity and in what direction?N/