For my time shifting assignment, I decided to make a “horror movie” webcam filter which takes in a webcam image which 1) plays a normal video until it time shifts 700 frames back for about 100 frames (to give the unsettling video playback effect that some horror movies have) 2) turns the RGB values from the webcam into pure luminance values and then filters the luminance value to create a grainy/distorted image .

Gist of code: gist:6f11dc358eabb3a5d7dc3f2dba39f493

In my program, I time-shifted the various color layers (RGB) from the camera feed to create a delayed-vibrant effect. I also implemented code to randomly change the dimensions of the camera feed every 0.5 seconds and used feedback to make an echo-like audio effect.

This is a simple example of feeding back output to input in a software we use very often – File Zipping.

Ever seen a file’s size increasing when you zip the file? In the video below, I have tried to give an example. Interestingly, if we zip an already zipped file something unusual happens, and the erratic behavior of the algorithm goes on as we keep on going in this loop of “zipping a zip”.

In the above video, I demonstrated the gradual degradation of the speech-to-text feature on my android phone. Using the original text from our assignment, I used Convert Text to Speech V2 on my laptop to convert the text to a sound file. I played the sound from my laptop speakers and recorded it directly to speech-to-text on my phone using facebook messenger (just for speedy transfer back to my laptop).

I fed this text back to the Convert Text to Speech V2 software, and did this loop for 20 iterations. I’d had the phone on my lap for most of the trials, and around version 14, I shifted positions and leaned back in my chair. That change of distance from the laptop speakers introduced more errors into the speech-to-text. I maintained that distance for the rest of the trials, which led to weirder misinterpretations. At the end of the video you can see how drastically the text had changed from the original passage.

The first idea I had for this assignment was to feed my name into the Wu-Tang Clan Name Generator and then feed the input of that back into itself over and over I did this 5 times and got the following:

Arnav Luthra -> Arrogant Menace -> Lazy-assed Killah -> Vizual Professional -> Annoyin’ Bstrd -> Shriekin’ Hunter

I didn’t really get any meaningful insight into the system so I decided to do something else.

Similar to I Am Sitting in a Room, I recorded myself briefly speaking but then fed it though Max for Live’s convolution reverb(set with the impulse of a PVC pipe) and repeated the process to yield the following:

It sounded harsher overall compared to the original I Am Sitting in a Room. This could be due to the resonant frequencies of a PVC tube. Also there was a certain warmness in the original that likely stemmed from some form of tape distortion.

Over the years, Amazon has given me quite a few interesting results under the “Customers who bought this item also bought” tab, some suggestions that have resulted in me impulsively buying more items online and others that have left me just plain confused. Therefore, I thought it would be interesting to explore this suggested purchases feedback loop.

As I have been recently setting up my new townhouse, I thought I would start with a simple search on Amazon for “office chair.” I then recorded the first suggested item of the list and then clicked on it to create the chain. If I encountered a situation where the suggested item looped, I simply recorded the next in line suggested item that I had not previously clicked on and continued down the chain. I followed the chain to about 50 items, by which time I had not seen an office chair for quite a while.

The video of the path across Amazon is below:

The system I selected was the information database, Wikipedia. I started with a simple search term, Signal, and followed what I thought were interesting links from page to page. Many times I have gone down the wormhole that is Wikipedia links, and ended up nowhere close to the topic I started off on.

This particular trip took me from internet history, to U.S governance, back to computer science, to Carnegie and Mellon, to golf, to horse shows, to obsessive compulsive disorders, to polio, to history, to math, to data, to film, to mythology, to national disasters, and finally currency.

The system I chose to work with was Youtube.  I clicked on the #1 video in the trending section, and it was “Taylor Swift – …Ready For It? (Audio).” I then clicked the 5th video in the up next sidebar. I repeated the process 25 times, but prevented videos that I had chosen earlier from showing up. The 25th video was “Teen Titans Go Transforms Baby Raven Starfire Growing Up Surprise Egg and Toy Collector SETC” which had no relation to Taylor Swift, or even music for that matter. Through this process, I destroyed any evidence of the original video through feedback.

Links to first and last videos:

https://www.youtube.com/watch?v=T62maKYX9tU

https://www.youtube.com/watch?v=I4HLidGdVmY