For my assignment, I first used my capture card to record part of the opening to the game The Legend of Zelda: Majora’s Mask. I then transferred that footage to my laptop, where it was output and recorded back to my main computer. I then repeated this rerecording from my laptop many times. So as to prevent the video from being destroyed by the darkening of the footage alone, I added a bit of brightness after each iteration. Additionally, each recording was converted from AVI to MP4 before being iterated on. I successfully recorded a total of 16 iterations, at which point the video component was destroyed.

Since the file is too large to upload here. I have uploaded it to youtube: https://youtu.be/yrOUTDxm8y4

Since arriving at CMU, I’ve gotten exposed to recording and editing audio in Pro Tools, but I have never really tried to make a sound anything but album-ready. This being the case, I wanted to explore what kind of negative effects something like a simple delay (often used in small doses to make vocal tracks or other instrumental tracks sound like there is a “glitch” in the processing system or some other cool effect) could have on a vocal track. So, I recorded myself saying one phrase, then repeatedly added the smallest possible delay to the audio, duplicated it, and continued the process until it became very hard to pick out what I was saying. Frankly, I stopped at the point where I couldn’t stand how screechy the audio became.

What resulted is a clip that both annoys you with my voice and makes your ears want to bleed so enjoy, or just invest in some ear plugs, up to you!

I’ll also add that as the trials went on and on, the signal started clipping to an INSANE degree. I had to use a limiter on every clip from about the fourth repetition on. So if you think this is bad, imagine what it would’ve sounded like naturally.

Inspired by another project I am working on, I decided to utilize an online beauty tool which is designed to make your photos “more attractive.”  The best one I found for my purposes is www.pinkmirror.com. There is an option to alter the face based on the gender binary, and to screw with the site more, I selected female. I provided the site with an image, got an “improved image,” and fed that image back in. Below is the original image followed by 20 iterations.

I am so impressed with the results. I was surprised at how quickly it closed up to my nose. Thorugh the process, it asks whether or not the face is male or female, and gives a default based on what the site thinks. I had to correct the site to female, but only for the first 3 iterations. After 3 it began recognizing it as female, until iteration 17, when it began seeing it as male again. The other interesting artifact, is that the process failed to place the watermark in the same place on every image.

Audacity is a good program! Among many other things, it provides fun effects like Paulstretch! I’m not quite sure who Paul is, but his stretching algorithm sure does a good job of making things sound sloooowwwwmooooooooooooooooo

Or, you can set the “Stretch Factor” to 1 and forego the stretching of the clip, instead simply making it sound more… Paul-y. So that’s what I did. With a “Stretch Factor” of 1 and a “Time Resolution” of .25 seconds, I fed the same clip into the Paulstretch effect 30 times until it was a) pretty quiet and b) quite eerie. If I had to guess, I’d say Paul is a cute, timid ghost – Like Casper, but an audio engineer. Thanks Paul!

Always fascinated by the beauty of nature and the idea of being able to make unnatural alterations, I enjoyed this process of simultaneous creation and destruction. A video from my visit to Kariyush Beach in Okinawa was utilised to create this piece.

The original video after color adjustments:

In order to modify the visual elements of the original video, I generated a crystalize cell pattern using Adobe Premiere Pro CC, increasing the cell size by 10 every second. Although the pattern creates geometric forms that evidently add a sense of obscurity to the experience, the audio was not heavily altered, as I enjoy maintaining parts of what any natural form offers. The speed was decreased by 10% each generation, and as a result, the initially refreshing sounds of waves become heavier and uneasier to bear one step after another:

This is the final product: 

This function shifts the frequency, and by connecting the output to the input, the function is going to be applied on the shifted frequency.

The other day in a computer science class, I came across a funky-looking function that made my head spin trying to understand it. I wanted to try to emulate that feeling by taking an image of that formula and using Python’s image rotation function to rotate the image 41 degrees at a time. I chose 41 degrees because I wanted something that would not divide 360 degrees evenly. Pretty soon, we start to see the effects of resampling the image at every rotation. We also see the edges of the image rounding out as corners are clipped off at each rotation.

This video shows 30 iterations using the nearest pixel’s value for resampling. (The image didn’t seem to change much more after 30 iterations)

Out of curiosity, I also tried playing with the other two possibilities for resampling. This is what the image looked like after 100 iterations of bilinear resampling (linear interpolation in a 2×2 environment):

And this is what it looked like after 100 iterations of bicubic resampling (cubic spline interpolation in a 4×4 environment):

My code:

Over this past summer, I tried to diversify my taste in music, and one of the ways I did so was by creating stations from songs I like. The idea of using music streaming services to create new stations from an existing song reminded me a lot of the Google Image video we watched, and so I got the idea to create a playlist starting with a song I liked (“Dance Apocalyptic” by Janelle Monáe), creating a station from that song, adding the new song the station played to the playlist, and repeating the process with the new song.

Like the Google Image video, I also had some constraints. Firstly, new stations frequently started with the song they were originated from, so if this happened I would skip the song and go to the next one. I also didn’t want repeat songs later, so if a song that was already on the playlist appeared, I would do the same thing.

Here is the playlist my project created!

The playlist ended up traversing many different genres, which was really cool and fun to watch as it transitioned gradually but ended up going in a lot of different genres. Here is a simplified overview of how the playlist progresses, with a sample songs from the playlist:

1. Contemporary R&B/Funk – “Dance Apocalyptic” by Janelle Monáe
2. 90s/2000s R&B/Rap – “Only You-Bad Boy Remix (feat. The Notorious B.I.G. & Mase)” by 112, The Notorious B.I.G., Mase
3. Contemporary Rap – “F**k With You” by Pia Mia, G-Eazy
4. Pop R&B/Rap – “I’m the One (feat. Justin Bieber, Quavo, Chance the Rapper & Lil Wayne)” by DJ Khaled, Justin Bieber, Quavo, Chance the Rapper, Lil Wayne
5. Pop – “Wildest Dreams” by Taylor Swift
6. 2000s Acoustic Rock – “I’ll Be” by Edwin McCain
7. 2000s Pop – “Irreplaceable” by Beyoncé
8. Pop – “We Can’t Stop” by Miley Cyrus
9. 2000s Hip Hop – “Sexy Can I feat. Yung Berg” by Ray J
10. Alternative R&B – “Just Friends (Sunny)” by Musiq Soulchild
11. Acoustic/Indie – “feelings are fatal” by mxmtoon
12. Alternative/Indie – “Let Me Die” by Lil Happy Lil Sad
13. Pop Punk – “GRRRLS” by AViVA
14. Acoustic/Pop – “Colour Me” by Juke Ross

I decided to use Thesaurus.com to generate synonyms for words, and see how far from the initial word definition that multiple iterations would take me, essentially destroying the original meaning. I had a constraint, similar to the Google Image recursion video we saw in class. If I saw a word once, I would not go back to it, and would instead pick the next best synonym. I started with the arbitrary word “bright”, and generated synonyms for it, and so on for 40 iterations. Here is a selection of the iterations:

Fifth iteration: “flaming”

Tenth iteration: “bloom”

Twentieth iteration: “smirk”

Thirtieth iteration: “bomb”

Fortieth iteration: “bombing run”

As can be seen, by the tenth iteration, the meaning was already distorted, and it continued on from there. Here’s a link to a PowerPoint showing each iteration, if anyone wants to see the specifics!