Sieves is an audio-visual series where continuously shifting image scans are interpreted as the parameters for spectral audio signal processing. What is heard as the soundtrack of the video is the video itself interpreted as a sonic effect. In this series, the audio processed through the visual is from field recordings of environmental noise. On screen, rows of pixels are constantly updated to reveal new variations of intensity information evident as grayscale values. This data is utilized to control frequency domain audio filters that enhance or reduce spectral content in recorded sound.

The process may be described as a graphically dependent form of subtractive synthesis or noise shaping where analyzed pixel data becomes a tool to sculpt frequency-rich sound sources. Here, grayscale values moving towards black suppress content, while values approaching white reveal it. Graphic form is listenable as new sound shapes are revealed.

Sieves was featured in the Controlled Evidence exhibition in November-December of 2012 at the Simons Center for Geometry and Physics at Stony Brook University. In this exhibit, three selections from the series ran in tandem and asynchronously resulting in chance, multichannel correspondences.