Brian Griffith

MFA 1 - Music

Biological Internal Feedback

Biological Internal Feedback is a visual music piece exploring the opaque gelatin that is created when combining the moment of inspiration and the realization of the thought. The video for this piece was created using nature footage taken from around my neighborhood, and abstract video synth textures created in the CalArts Videographics Lab. Aesthetically, the two pieces of video are quite different, however, it is during these peaceful walks through Elysian Park when I start to think of the textures and shapes possible with video synthesis. The audio is also inspired by these walks: processing fields recordings that have been recording ad hoc, and composing elements that mimic the motion or sound of these recordings.

The result is the first in an ongoing series of personal exploration that aims to get at the distilled essence of creativity and expression. By exploring and combining elements that ordinarily might by two distinct styles, the commonalities will blossom.

 

Ward Melnikoff

Return Of The Radiolarian After a 20 year pause, my Radiolarian Landscapes are returning. http://wardmelnikoff.com/

Charles Danner

TW/CW: trauma **Trigger Warning** This is a multimedia piece about the experience of being a victim of trauma in the current COVID-19 climate. The piece consists of original poetry written and performed by Nicole Paige Chaffin over an original score by Charles Van Alst Danner. The score features a custom feedback instrument, the FeedBox in […]

Perry Cook

COVID Pan Drum: A Robot Tongue-Drum Rendering of the SARS-2 COVID Virus Genome I’m building a custom robot to play a little lap-sized tongue (steel) drum. A program will read through the roughly 30k base pairs in the COVID 19 (SARS-2 COVID Wuhan Seafood Market) DNA sequence. Particular (known) functioning segments will be sonified when […]

Sonia Vargas

Lights Lights of a carousel illuminating the dark sky https://syvargas470.wixsite.com/website

Dongpu Ling

ML Landscape I am interested in the inaccurate and unpredictable result that a machine can make. In order to understand its “mind”, I train the machine using images that have not been cropped, to see how it understands a thing that has not be seen before.