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M. Yee-King ,M. Inverno, P. Social machines for education driven by feedback , in Proceedings First International Workshop on the Multiagent Foundations of Social Computing, AAMAS-2014, Paris, France, May 6 2014,
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M. Yee-King ,M. d'Inverno Pedagogical agents for social music learning in Crowd-based Socio-Cognitive , in Proceedings First International Workshop on the Multiagent Foundations of Social Computing, AAMAS-2014, Paris, France May 6 2014,
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M. Yee-King, M. Krivenski, H. Brenton, A. Grimalt-Reynes, M. d’Inverno.
Designing educational social machines for effective feedback. 8th International Conference on e-learning. Lisbon, Portugal, 15-18 July, 2014.
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Matthew Yee-King, Roberto Confalonieri, Dave De Jonge, Katina Hazelden, Carles Sierra, Mark d'Inverno, Leila Amgoud, Nardine Osman `Multiuser museum interactives for shared cultural experiences: an agent-based approach' Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
ACM digital library link
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Livecoding for SuperCollider and live alto flute: Matthew Yee-King and Finn Peters
Computer Music Journal DVD, Winter 2011, Vol. 35, No. 4, Pages 119-137
An autonomous timbre matching improviser Matthew John Yee-King, ICMC 2011
See it online
Progress Report on the EAVI BCI Toolkit for Music: Musical Applications of Algorithms for use with consumer brain computer interfaces
Mick Grierson, Chris Kiefer, Matthew Yee-King, ICMC 2011
A Comparison of Parametric Optimization Techniques for Musical Instrument Tone Matching
Yee-King, Matthew, Roth; Martin, 130th AES convention, 2011
Parametric optimisation techniques are compared in their abilities to elicit parameter settings for sound synthesis algorithms which cause them to emit sounds as similar as possible to target sounds. A hill climber, a genetic algorithm, a neural net and a data driven approach are compared. The error metric used is the Euclidean distance in MFCC feature space. This metric is justified on the basis of its success in previous work. The genetic algorithm offers the best results with the FM and subtractive test synthesizers but the hill climber and data driven approach also offer strong performance. The concept of sound synthesis error surfaces, allowing the detailed description of sound synthesis space, is introduced. The error surface for an FM synthesizer is described and suggestions are made as to the resolution required to effectively represent these surfaces. This information is used to inform future plans for algorithm improvements.
Download: Download the thesis chapter this paper was a short version of... A Comparison of Parametric Optimization Techniques for Musical Instrument Tone Matching PDF
AES library link
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Matthew Yee-King, Martin Roth, International Computer Music Conference 2008.
This work presents a software synthesizer programmer, SynthBot, which is able to automatically find the
settings necessary to produce a sound similar to a given target. As modern synthesizers become more capable and the
underlying synthesis architectures more obscure, the task of programming them to produce a desired sound becomes more
time consuming and complex. SynthBot is presented as an automated solution to this problem. A stochastic search
algorithm, in this case a genetic algorithm, is used to find the parameters which produce the most similar sound to
the target. Similarity is measured by the sum squared error between the Mel Frequency Cepstrum Coefficients (MFCCs)
of the target and candidate sounds.
The system is evaluated technically to establish its ability to effectively search the space of possible parameter
settings. A pilot study is then described where musicians compete with SynthBot to see who is the most competent
synthesizer programmer, where each competitor rates the other using their own metrics of sound similarity. The
outcome of these tests suggest that the system is an effective "composer's assistant".
Click the title to download.
Matthew Yee-King. MusiCAL workshop, 9th
European Conference on Artificial Life, September 2007.
Evolving through a series of target kits, namely TR606 - TR707 - TR808 - TR909.
The expectation of the listener from house and techno music seems
to be that percussion sounds will maintain the same timbre for the
duration of a piece of music. For the composers of such musics the
synthesizing of drum sounds of a quality equal to those available
from commercial drum machines or samples is difficult and seems unnecessary.
A system is presented here which provides a unique method for the
composition of rhythmic patterns with dynamic timbres. A genetic algorithm
using a heterogeneous island population model is applied to the problem
of percussion sound synthesizer design. Multiple percussion sounds
are evolved simultaneously towards different targets where the targets
are audio files specified by the user. The fitness function driving
the evolution compares the evolving sounds to the target sounds in
the frequency domain, awarding higher scores for closer matches. The
system was tested using a simple step sequencer interface, as found
in classic drum machines and a MIDI controlled version has also been
implemented. The system provides the user (and listener) with a tangible
sense of timbral transformation as the performance proceeds, where
the timbres move ever closer to the target sounds. This represents
an effective application of an artificial life technique to real time,
algorithmically enhanced music composition.
An Automated Music Improviser Using a Genetic Algorithm Driven Synthesis Engine
Matthew Yee-King. Presented at the EvoMusart workshop, evo* 2007, published in Applications of Evolutionary Computing,
volume 4448 of Lecture Notes in Computer Science (LNCS 4448), pages 567577. Springer,
Genetic algorithm sound synthesizer sound example (featuring Finn Peters on sax, Tom Skinner on drums and CPU+my algorithm on synth sounds).
The improviser on its own, playing against an Eric Dolphy solo
This paper describes an automated computer
improviser which attempts to follow and improvise against the
frequencies and timbres found in an incoming audio stream. The
improviser is controlled by an ever changing set of sequences which
are generated by analysing the incoming audio stream (which may be a
feed from a live musician) for its physical and musical properties
such as pitch and amplitude. Control data from these sequences is
passed to the synthesis engine where it is used to configure sonic
events. These sonic events are generated using sound synthesis
algorithms designed by an unsupervised genetic algorithm where the
fitness function compares snapshots of the incoming audio to snapshots
of the audio output of the evolving synthesizers in the spectral
domain in order to drive the population to match the incoming
sounds. The sound generating performance system and sound designing
evolutionary system operate in real time in parallel to produce an
interactive stream of synthesised sound. An overview of related
systems is provided, this system is described then some preliminary
results are presented.
Virtual and Physical Interfaces for Collaborative Evolution of Sound
Sam Woolf and Matthew Yee-King, Contemporary Music Review, Volume 22, Number 3 / September 2003
Interactive evolution with genetic algorithms can be used to facilitate the rapid development of interesting sonic forms. This paper describes two rather different and innovative systems that allow multiple users to evolve sound collaboratively. The Sound Gallery was conceived as an interactive installation artwork where the movements of a group of physically present participants are tracked over time and influence the evolution of sound-modifying hardware circuits. AudioServe is a tool that allows visitors to a web-based interface to evolve sounds by mutating virtual frequency and amplitude (FM/AM) modulation circuits left on the server by previous users. The two projects eventually became linked when the physical interface system designed for the Sound Gallery was connected to an adapted version of the audio-synthesis engine built for AudioServe. The two systems are described, the techniques used to create them are explained and some of the issues involved in collaborative sound evolution are discussed.