Eduardo Reck Miranda

Evolutionary Computer Music

An Introduction to Evolutionary Computing for Musicians.- Evolutionary Computation for Musical Tasks.- Evolution in Digital Audio Technology.- Evolution in Creative Sound Design.- Experiments in Generative Musical Performance with a Genetic Algorithm.- Composing with Genetic Algorithms: GenDash.- Improvizing with Genetic Algorithms: GenJam.- Cellular Automata Music: From Sound Synthesis to Musical Forms.- Swarming and Music.- Computational Evolutionary Musicology.

A new model of sensorimotor coupling in the development of speech

We present a computational model that learns a coupling between motor parameters and their sensory consequences in vocal production during a babbling phase. Based on the coupling, preferred motor parameters and prototypically perceived sounds develop concurrently. Exposure to an ambient language modifies perception to coincide with the sounds from the language. The model develops motor mirror neurons that are active when an external sound is perceived. An extension to visual mirror neurons for oral gestures is suggested.

A survey of computer systems for expressive music performance

We present a survey of research into automated and semiautomated computer systems for expressive performance of music. We will examine the motivation for such systems and then examine the majority of the systems developed over the last 25 years. To highlight some of the possible future directions for new research, the review uses primary terms of reference based on four elements: testing status, expressive representation, polyphonic ability, and performance creativity.

Expressivity of voice synthesis by emphasizing source signal features

Voice synthesis with improved expressivity is obtained in a voice synthesiser of source-filter type by making use of a library of source sound categories in the source module. Each source sound category corresponds to a particular morphological category and is derived from analysis of real vocal sounds, by inverse filtering so as to subtract the effect of the vocal tract. The library may be parametrical, that is, the stored data corresponds not to the inverse-filtered sounds themselves but to synthesis coefficients for resynthesising the inverse-filtered sounds using any suitable re-synthesis technique, such as the phase vocoder technique. The coefficients are derived by Short Time Fourier Transform (STFT) analysis.

A framework for the evaluation of music representation systems

Stable URL:http://links.jstor.org/sici?sici=0148-9267%28199323%2917%3A3%3C31%3AAFFTEO%3E2.0.CO%3B2-%23Computer Music Journal is currently published by The MIT Press.Your use of the JSTOR archive indicates your acceptance of JSTORs Terms and Conditions of Use, available athttp://www.jstor.org/about/terms.html. JSTORs Terms and Conditions of Use provides, in part, that unless you have obtainedprior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content inthe JSTOR archive only for your personal, non-commercial use.Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained athttp://www.jstor.org/journals/mitpress.html.Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printedpage of such transmission.The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academicjournals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers,and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community takeadvantage of advances in technology. For more information regarding JSTOR, please contact support@jstor.org.http://www.jstor.orgTue Jan 29 22:04:20 2008

Making Music with Algorithms: A Case-Study System

19 Computer Music Journal, 23:2, pp. 19–30, Summer 1999

Granular Synthesis of Sounds by Means of a Cellular Automaton

Chaosynth is a new sound synthesis system being developed by the author and others working at Edinburgh University. Chaosynth functions by generating a large amount of short sonic events, or particles, in order to form larger, complex sound events. This synthesis technique is inspired by granular synthesis. Most granular synthesis techniques, however, use stochastic methods to control the formation of sound events, while Chaosynth uses a cellular automaton. Following an introduction to the basics of granular synthesis, the author explains how Chaosynth’s technique works. He then introduces the basics of cellular automata and presents ChaOs, the cellular automaton used in Chaosynth. The article concludes with some final remarks and suggestions for further work.

Interfacing the Brain Directly with Musical Systems: On Developing Systems for Making Music with Brain Signals

The authors discuss their work on developing technology to interface the brain directly with music systems, a field of research generally known as Brain-Computer Interfacing (BCI). The paper gives a brief background of BCI in general and surveys various attempts at musical BCI, or Brain-Computer Music Interface (BCMI) systems designed to make music from brain signals, or brainwaves. The authors present a technical introduction to the electroencephalogram (EEG), which measures brainwaves detected by electrodes placed directly on the scalp. They introduce approaches to the design of BCI and BCMI systems and present two case study systems of their own design: the BCMI-Piano and the Inter-Harmonium.

Neural correlates of emotional responses to music: An EEG study

This paper presents an EEG study into the neural correlates of music-induced emotions. We presented participants with a large dataset containing musical pieces in different styles, and asked them to report on their induced emotional responses. We found neural correlates of music-induced emotion in a number of frequencies over the pre-frontal cortex. Additionally, we found a set of patterns of functional connectivity, defined by inter-channel coherence measures, to be significantly different between groups of music-induced emotional responses.

Brain-Computer Music Interfacing (BCMI): From Basic Research to the Real World of Special Needs

This paper reports on the development of a proof-of-concept brain-computer music interfacing system (BCMI), which we built to be tested with a patient with Locked-in Syndrome at the Royal Hospital for Neuro-disability, in London. The system uses the Steady State Visual Evoked Potential (SSVEP) method, whereby targets are presented to a user on a computer monitor representing actions available to perform with the system. Each target is encoded by a flashing visual pattern reversing at a unique frequency. In order to make a selection, the user must direct her gaze at the target corresponding to the action she would like to perform. The patient grasped the concept quickly and rapidly demonstrated her skill at controlling the system with minimal practice. She was able to vary the intensity of her gaze, thus changing the amplitude of her EEG and vary the consequent musical parameters. We have proved the concept that such a BCMI system is cost-effective to build, viable, and useful. However, ergonomic and design aspects of the system require further refinement in order to make it more practical for clinical usage. For instance, the system at present requires a therapist to place individual electrodes and calibrate a user’s response to each stimulus, which can be time consuming. A new version of the system will require just positioning of a headset and, due to advanced algorithms, will require no calibration.