In 2012 I created my first interactive touch wall: Touch #1. The work built on my experience creating the visuals for Still Life and was largely inspired by seeing autistic children experiencing pure joy while interacting in an immersive environment.
Touch #1 received a great response and was later installed at Exploration Place and at Living Arts Gallery in Tulsa. Touch #1 was also the inspiration for my next touch wall, Touch #2.
Auralis is a musical instrument constructed from a multitouch table and custom software ported from my earlier touch wall Touch #2. Merging sound, interactivity, and sequencing, Auralis is simultaneously engaging, meditative and soothing. I created the work for youth ages 8-14. A virtual world of suns, stars and planets interact with each other. Participants run…
Around 2004 I developed a few protoype microphones enhanced to also offer proximity detection. The microphone could adjust it’s amplitude and bass response based on the proximity of the person using it. This would lessen the variable results users experience when holding a microphone too close or too far. Moreover, with proximity or its derivative mapped to a combination…
As part of the Ballet Wichita’s Innovations performance April 2024 I teamed up with Wichita State University’s Shocker Studios to track a dancer live, reinterpreting their motions on a stage-sized display behind them. Wichita State University’s Fairmount String Trio accompanied the dance, performing Dohnányi’s Serenade. To track the dancer we used 3 Vive 3.0 trackers…
SoundBlocks is a tangible environment where youth connect blocks to describe network dataflow. The environment explores digital sound manipulation as a personal, meaningful and fun artistic endeavor, rather than as a venture into mathematical, electronic or networking relationships.
I built a connector to access the Gameboy circuitry using a solderless breadboard and used it to interface flash memory and a Digital to Analog Convertor (DAC) to the Gameboy.
I worked with VÃctor Adán to explore how if we might train a machine to recognize chords changes in music. We tried multiple models to solve the problem, including Support Vector Machines, Neural Networks, Hidden Markov Models, and a few variations of Maximum Likelihood systems.