Trigger warnings! Violence. Karens. Dead people. Hurt dogs. Racism.

During street protests in 2020, the police behaved especially erratically: their motorcycle gangs lurked out of sight, then would pounce without warning. They would snatch protestors into unmarked white vans. They would demand we move out of the roadway and into Mellon park, and then claim the park was closed, and pepper spray us and arrest us for trespass.

I began to listen to police radio while at the protests, to predict the police so as to protect the most precarious among us. For example, my international student friends couldn’t afford to be arrested, for fear of them being deported. So we’d hopefully have a heads up with a pounce was imminent.  I also heard them make fun of our protest and its cause: Black lives.

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Carnegie Mellon partnered with the Pittsburgh Police to develop and deploy predictive policing software, using racist historical “crime” statistics to predict future crime. This was used to send police into “hotspot” disproportionately Black neighborhoods. After outcry, CMU claimed that the algorithm did not consider race as a factor, not fooling our history students who will tell you that racist laws lead to residential segregation, and our machine learning students who will tell you that therefore, under the blind eyes of machine learning, location is race .

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So I built a system to record police scanner audio, used machine learning to transcribe it, and built a system to allow you to automatically search keywords and extract associated audio clips. I then fought with OpenAI’s Whisper to transcribe this – this is where most of my time was spent.

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November 20th, 2022 was a cold day. At 8:25AM, it was about 20F, and fairly windy. I pressed the button to begin recording – and left it alone for  13 hours and 24 minutes.

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06:18:55 – 06:19:00 “There’s no description of the male other than him being a black male.”

I wanted to know how police hear and speak about people. Predominantly and entirely unsurprisingly, individuals are collapsed into a gender and race description:

               white:        Black:        total
Male:          8             9             95 
Female:        1             5             81
total:         38            43

Read all transcript snippets containing white, Black, male, or female here.

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But also the more benign: dogs?

We hear about lost dogs, found dogs, threatening dogs, and threatened dogs. Read all dog transcript snippets here.

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But like many beholders of algorithmic systems with a wealth of imperfect data, I struggle to know what meaning to intuit from it. Therefore, instead of presenting this as a finished piece, my canvas on which the last brush stroke has been cast, this is much more humble: a tool, for two people to make meaning together. This collaborative process is also in response to talking with Kyle McDonald, who suggested this become a tool for others to use, rather than a medium for a particularly situated “artist”.

I draw upon the feminist theorist Donna Haraway, and her notion of Situated Knowledges. Instead of attempting to assert a “view from nowhere”, instead: “feminist objectivity means quite simply [speaking from our] situated knowledges“. The result is knowledge that is “partial, locatable, critical” and much more honest than the once and for all facts declared in positivist science.

Under this tradition of ML, it makes sense to approach this as an epistemologically interpretive process – a far cry from the unwitting positivism of implied by the machine learning papers I read – and expect meaning to emerge through intersubjectivity. This then becomes an interactive, collaborative art piece, for people to make meaning together. This allows “located accountability” in technology production, that is, it allows us to be be known as the source of our knowledge claims, allows a discursive process to unfold, instead of presenting evident “truth” or “reality”.

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Link to live demo. 

How to use this:

1. Press CMD+F, and search for a term.
2. Click the link in the “Link” column
3. Leave an anonymous comment. What does the snippet mean to you? What situated knowledge leaves you
4. Respond to another’s comment. What intersubjectivity do you share? What don’t you?

This interactive art project uses kinect and openframeworks to capture the patterns on participants’ clothes as a 3D point cloud mesh.

Original idea and set-backs:

The project originally hoped to capture the most saturated color of a participant’s wearing and imprint these colors onto a palette. However, random artifacts introduced by Kinect would produce incorrectly highly saturated colors. So the idea was updated.

Set-up and Process:

The project uses Kinect to detect people within a distance of 500 to 1500 mm (0.5 to 1.5 m) and to produce point cloud information. Using openframeworks, the point cloud information can be drawn on canvas in real time.

The code allows the program to take a “screenshot” of the current window. Then, using ofxCv from kylemcdonald addon, the program can detect and contour out an area of some specific hue. By choosing wanted hue and adjusting tolerance threshold, the program can grab an area of pattern on the detected person. It then adds the current wanted patterns to a public ofMesh variable Imprint. 

This interactive project therefore allows creating an imprinted point cloud collage of clothing patterns.

What: I’m going to use depth analysis to create a “3D version” of 2D printings – which can create a different perspective to experience the paintings in the museums.

Technique: Depth Analysis + Blender

depth analysis can create a really interesting outcome with perspective drawings.

– Test 1

Test2: Edward Hopper

These are some rough drafts I created and tested. One problem is that they are somehow ragged, and might need further editing with the surface details.

I vision the final project could be 1. a combination with AR techniques that people can dynamically viewing the model from multiple perspectives or 2. Transport the blender to unity for 360 experience.

I’ve been playing around with a few things unrelated to the deliverables and at Golan’s request, will put the results here.

Slitscanning(ish) (slow motion)Video

Algorithm: for the i-th column of the video, take every i-th frame of the original video. (e.g. the right-most column is playing at c * 1024 FPS and the left-most column is playing at c FPS for some constant c.) Hold the last frame if a given column runs out of frames.

Shot on the edgertronic.

Digital NIR false color

The goal is to recreate the look of Aerochrome using a NIR digital camera.

Algorithm: take two photos of the same scene, one with IR high pass filter and a low pass filter. The first one with the low pass is a regular RGB photo and the high pass will give 1 NIR channel (effectively). Then use NIR channel for R, R for B, B for G in constructing a new image.

Interested in Bauhaus’ study of geometry shapes done by Oskar Schlemmer, I want to study more about the relationship between abstract geometry and the human body.

This project is a person dancing,  wearing different forms of geometry on their body, improvising to a piece of music, and trying to express their feeling with the shapes they are wearing.

I played with the frame rate and the transparency of the video .

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Challenges:

1. The silhouette of the human body can distract people’s attention on the geometry shapes.
2. The original videos are too large for my computer to process so only a few short clips are successfully edited 🙁

“Person in Time” made by Martha’s Cat Mug:

Isn’t it great?