Music Creation Using Cellular Automata

Tato práce se zabývá využitím celulárních automatů v oblasti algoritmické kompozice hudby. Text práce obsahuje stručný přehled problematiky algoritmické kompozice s využitím celulárních automatů a popisuje návrh a implementaci programu s grafickým uživatelským rozhraním, který používá dvě jednoduché metody převodu stavů celulárních automatů na posloupnost tónů. Výsledný program byl otestován s využitím několika celulárních automatů a kvalita zvukových výstupů obou metod byla vzájemně porovnána. Program je použitelný zejména pro vytváření krátkých melodií.In this thesis the cellular automata are used for algorithmic music composition. It contains a general overview of the algorithmic composition method and cellular automata. The crucial part of the thesis describes the concept and implementation of the program with a graphical user interface which uses two simple methods for conversion cellular automata's states into music. The final program was tested on several cellular automata and the sound outputs of both methods were compared with each other. The program is useful especially for the creation of short melodies.

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

Capturing the dynamics of cellular automata, for the generation of synthetic persian music, using conditional restricted Boltzmann machines

© Springer International Publishing AG 2017. In this paper the generative and feature extracting powers of the family of Boltzmann Machines are employed in an algorithmic music composition system. Liquid Persian Music (LPM) system is an audio generator using cellular automata progressions as a creative core source. LPM provides an infrastructure for creating novel Dastgāh-like Persian music. Pattern matching rules extract features from the cellular automata sequences and populate the parameters of a Persian musical instrument synthesizer [1]. Applying restricted Boltzmann machines, and conditional restricted Boltzmann machines as two family members of Boltzmann machines provide new ways for interpreting the patterns emanating from the cellular automata. Conditional restricted Boltzmann machines are particularly employed for capturing the dynamics of cellular automata

Cellular Automata and Music: A New Representation

For millenia, we’ve thought of musical composition as a purely human activity. However, we once also thought of an activity like chess to be purely human, but Deep Blue was able to defeat Kasparov in 1995 all the same. Could there perhaps be some tool or algorithm for musical composition that can replicate to some extent what human beings can do with music? This project explores this idea through the use of a tool called a cellular automaton. A cellular automaton is a grid space with a finite number of states for each of the ”cells” or ”squares” where a simple rule is applied, and through this rule amazingly complicated patterns emerge over many time steps. We tested the potential of these systems for choosing when and what notes to play in a musical composition. In this project, we mainly focused on creating a translator between music and cellular automata that matches music theory as closely as possible. Whether or not the tracks produced are musical or not could shed light on the computer’s ability to replicate high-level human activities

Exploitation of Memetics for Melodic Sequences Generation

Music, or in narrower sense, melodic contours of the aesthetically arranged pitches and the respective durations attracts our cognition since the beginning and now shaping the way we think in the complex life of culture. From evolutionary school of thoughts we could learn our perspective of seeing the musical diversity of folk songs in Indonesian archipelago by hypothesizing the aligning memes throughout the data sets. By regarding the memeplexes constructed from the the Zipf-Mandelbrot Law in melodic sequences and some mathematical characteristics of songs e.g.: gyration and spiraling effect, we construct evolutionary steps i.e.: genetic algorithm as tools for generating melodic sequences as an alternating computational methods to model the cognitive processes creating songs. While we build a melodic-contour generator, we present the enrichment on seeing the roles of limitless landscape of creativity and innovation guided by particular inspirations in the creation of work of art in general

Audio scrambling technique based on cellular automata

The final publication is available at Springer via http://dx.doi.org/10.1007/s11042-012-1306-7Scrambling is a process that has proved to be very effective in increasing the quality of data hiding, watermarking, and encryption applications. Cellular automata are used in diverse and numerous applications because of their ability to obtain complex global behavior from simple and localized rules. In this paper we apply cellular automata in the field of audio scrambling because of the potential it holds in breaking the correlation between audio samples effectively. We also analyze the effect of using different cellular automata types on audio scrambling and we test different cellular automata rules with different Lambda values. The scrambling degree is measured and the relation between the robustness and the scrambling degree obtained is studied. Experimental results show that the proposed technique is robust to data loss attack where 1/3 of the data is lost and that the algorithm can be applied to music and speech files of different sizes.This work is partially supported by the Spanish Ministry of Science and Innovation under coordinated research projects TIN2011-28260-C03-00 and TIN2011-28260-C03-02 and by the Comunidad Autónoma de Madrid under research project e-madrid S2009/TIC-165