I used the Face App to make myself into the Ultimate Woman using the “young” filter, the “smile” filter, and the “female” filter.
This ^ is the original picture.
I tried to cycle the pictures through it until I got to here:
To get the *Ulitmate* ageless look, I used the “young” filter. Here’s what it’s like to be 30 times younger:
Cycle 4
Cycle 10
Cycle 20
Cycle 30
I’ve been told I should smile more, so naturally I thought I would include it in the criteria. Here’s what a 104 times processed *Ultimate* smile looks like:
Cycle 3
Cycle 20
Cycle 70
Cycle 104
I also used the “female” filter. Heres what a it looks like processed through 8 times:
I used an online voice changer (https://voicechanger.io/) as a system to modify the song Here It Goes Again by OK Go. As the song was fed through, it was interesting to see which sounds/instruments kept their integrity the longest. Bass and middle notes seemed to fade out first, and the vocals were distinguishable almost until the very end. There were quite a few artifacts in the later editions, but I think that was part of using low tech digital equipment.
The process of uploading the song file and applying the filter was repeated only eight times until the song was completely unrecognizable.
I wanted to see where ifyoudig.net would take me. Ifyoudig.net is a website that returns the name of an artist it believes you will “dig” based off of your input of the name of an artist you already “dig.” If you were wondering, yes, this lingo is in fact killing me.
I started with Michael Jackson as the input artist and the original signal. Then I kept clicking the topmost artist in the list, the only artificial constraint being that I would not select an artist that I had already selected previously. Then I watched as the ifyoudig system did its magic.
After 45 iterations, the website was beginning to suggest artists like Austrian duo Kruder & Dorfmeister, known for their electronic downtempo sound.
After about 40 more iterations, the website suggested Yonder Mountain String Band, a progressive bluegrass group from Colorado.
I attached a video that takes you through the first 45 iterations by showing the major album of each successive artist/band – all to the backdrop of one of MJ’s most popular tunes. Then the video jumps to number 85 and includes an audio clip from one of Yonder’s most famous tracks. This provides you (the listener) with a side by side comparison of the sound of the input signal versus the sound of the output signal after 85 iterations of “feedback” through the system.
The system I used to process my signal through was an algorithm to Pixel Sort images for the application Processing. Pixel sorting is the process of isolating a horizontal or vertical line of pixels in an image and sorting their positions based on any number of criteria, in my case I sorted the pixels of the images through the brightness. In more detail, the script will begin sorting when it finds a pixel which is bright in the column or row, and will stop sorting when it finds a dark pixel. The script identifies black pixels by comparing the pixel’s brightness value to a threshold, if it’s lower than the brightness threshold the pixel is deemed to be dark, if it’s higher it’s deemed to be bright.
I started with a simple image of a bird-eye view picture of an island. I ran my image through the system 630 times to be exact. The effect or changes can be quite noticeable at the beginning, but they slowly become harder to notice as the signal gets twisted even more. I also noticed that the algorithm itself has a few bugs, leaving some blocks of pixels unsorted at all. Especially in the middle (middle of islands) where brightness levels are even and high, the effect won’t be present no matter how many times the process is ran.
For more info about pixel sorting, please see: http://datamoshing.com/2016/06/16/how-to-glitch-images-using-pixel-sorting/
For this assignment I took inspiration from the example of I am sitting in a room by Alvin Lucier by iteratively re-recording audio. The readymade system I used was Ableton Live. I took a default drum loop, recorded the audio using my computer speakers and a condenser mic, then applied a built-in audio effect called Redux which is essentially a bitcrusher/downsampler. I did this 11 times (including the original loop). It became somewhat painful to listen to since certain high frequencies were being amplified each pass.
An interesting and unintended side effect of the setup I used was the latency of the system. Each recorded section is exactly 3 measures at 70 BPM, but as you can hear the recordings become increasingly late and eventually cut off the end of the original loop.
I decided to use the built in photo editor in Android messenger as my found system. I started with a close up photo of my friend Miles and I repeatedly put it through the editor. The photo editor has multiple simplistic sliders such as contrast,shadows, and sharpness. In order to keep each individual jump relatively small, I turned each of these sliders only halfway up. The entire process took 45 iterations to reach the final picture.
The sharpness slider definitely had the most impact on the picture overall as its attempt to remove “blurriness” often resulted in the colors blending later on in the sequence. It is also interesting to see how the sliders changed as the photo changed. Towards the end of the process, the shadow slider which usually makes the darkness in the photo much more prominent, actually enunciated the whites in the photo.
The system I chose is the the “Facet” algorithm located in the Photoshop filter gallery. What I did is that I opened a picture in Photoshop, then applied the “Facet” filter onto the picture over and over so that the color and the composition of the pixels are transformed into something very different from the original.
It’s interesting to see how the result of this feedback system does not look like what I predicted at all. Please see the video documentation below to find out how the picture has evolved. The filter was applied for 1000 times.
This is a video that shows how Google Translation can distort the meanings of a poem by recursively feeding the translation results back to itself and translating to random languages 60 times. The original poem is in Chinese, and very well translated into English. The title is Facing the Sea with Spring Blossoms by HaiZi, a Chinese poet.
From tomorrow on, I will be a happy man.
Grooming, chopping and traveling all over the world.
From tomorrow on, I will care foodstuff and vegetable.
Living in a house towards the sea, with spring blossoms.
From tomorrow on, write to each of my dear ones.
Telling them of my happiness.
What the lightening of happiness has told me.
I will spread it to each of them.
Give a warm name for every river and every mountain.
Strangers, I will also wish you happy.
May you have a brilliant future!
May you lovers eventually become spouses!
May you enjoy happiness in this earthly world!
I only wish to face the sea, with spring blossoms.
After 33 random translations:
ur
You know you went to the world,
Cooking vegetables and vegetables
I am a source of water and water
In the morning, family relationships
Stories, history
It’s a good vaccine
Everyone says,
He was always his name
I do not believe
if you want to
Hence
the poor
Chicken in the ocean
After 60 random translations:
road
You know, I go in the world
All full of fruit
Water and Water Resources
It is very close to me in the morning
History, history
It is the perfect solution.
a total of
It is always the name of the
I always thought,
If you want to
yes
bad
Poland
This is the opening sequence of Alfred Hitchcock’s film, Rear Window. The original digitized (via YouTube) film clip was played on my computer screen using MPEG Streamclip and simultaneously screen recorded using QuickTime. The newly recorded version was then played via MPEG Streamclip and again screen recorded using QuickTime, and so on.
As the signal decayed, the video slowed, stuttered, became higher in contrast, and eventually darker and darker, resulting in the interesting illusion of the scene taking place later in the day with each subsequent rendering. After 18 iterations, the film became almost completely black, with only the appearance of what seem to be small fiery flares visible in the darkness.
The grid below is a composite of all 18 passes simultaneously – best viewed in full screen!
The above video is the result of a beautifying filter from a photo retouching app (Meitu) being applied to the same picture (of myself) about 120 times. The filter itself was the one the app called ‘natural’, set to be as subtle as possible. (A slider in the app alters how drastic the changes are.)
To follow the themes presented in the in-class examples, this project was meant to demonstrate certain fundamental artifacts and assumptions in the process itself – in this case, what does the app consider beautiful? We see in the final result of the process those artifacts extracted and brought to their extremes.
The music on the video doesn’t have anything to do with feedback loops, I just thought having it in the background was funny.
