All posts by Hetian C

Test Patterns by Ryoji Ikeda

Test pattern is a system that converts any type of data (text, sounds, photos and movies) into barcode patterns and binary patterns of 0s and 1s. Through its application, the project aims to examine the relationship between critical points of device performance and the threshold of human perception. In this first edition of the project, an audiovisual installation, test pattern involves a sequence of tests for machines and humans, comprising visual patterns converted and generated from sound waveforms in real–time. The installation comprises 8 computer monitors and 16 loudspeakers aligned on the floor in a dark space. The 8 rectangular surfaces of the screens flicker intensely with black and white images, floating and convulsing in the darkness. 16–channel sound signals are mapped as a grid matrix, passing and slicing the space sharply. Via a real–time computer program, the signal patterns are converted into 8 barcode patterns, which are tightly synchronized. The velocity of the moving images is ultra–fast, some hundreds of frames per second at certain points, providing a performance test for the devices and a response test for visitors’ perceptions.

See more on Ryoji Ikeda’s website: http://www.ryojiikeda.com

Waking Up and Up and Up…

Inspired by the movie Inception directed and written by Christopher Nolan, about using a special equipment called PASIV(Portable Automated Somnacin IntraVenous Device) that can allow a group of people to share a dream in order to steal hidden informations from the subconscious of certain target, I wanted to build something that can be represented by different layers and can be synchronized in some way just like the ideas in the movie, that people can have a dream inside a dream inside another dream. And as the dreamer foes deeper, the brain functions accelerate so time stretches, meaning that 5 minutes in reality is an hour in the first layer of dream, 12 hours in the second, 144 hours in the third… until the dreamer falls into limbo, which is infinity. In my project I tried to visualize the process of synchronization and the intensity of each layer as the process goes, and try to figure out a way to bring different layers together and connect them into a whole picture. And as I was looking for ideas, I happen to see some of the amazing pieces using MAX/MSP to do fascinating visualization from system developer Masato TSUTSUI (http://adsr.jp/works/). And I started to study how he built the geometries and was trying to use a similar method to develop this project. And I ended up using a lot of techniques from his piece mai_planets_b.
And here is a demonstration of how my system works.

demo

And the song I used in the demo that has three different time lapsing is Non Je Ne Regrette Rien sang by Edith Piaf from the movie. They used the song as a kick to wake up from a three-layers dream. In the beginning the slowest layer goes in strongest, then the less slow one joined followed by the normal speed of the same song. And in the end they just disappeared accordingly.

patch:


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-----------end_max5_patcher-----------

Assignment 5


----------begin_max5_patcher----------
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Spectral System: Concentrated But Ciaotic

I simply tried to change as many factors in the frequency domain as possible, and the final results are windy, crowded, but with a concentrated tone. When I played with the numbers in the system, the outcomes somehow sound more like wind bell.

Here are three samples of the system:

1. Abel Korzeniowski: Daydreams from the movie: A Single Man
The original:

Variation 1:

Variation 2:

2. “He Was A Beautiful Player” from the movie: Whiplash
The original:

Variation 1:

3. Dave Porter: Breaking Bad Main Title
The original:

Variation 1:

Variation 2:

Variation 3:


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Convolve it: Keep Marching Scot!!

I ran into the practice of CMU Kiltie Band the other day and was quite impressed by their positive and cheerful energy. And I remember thinking whether they would literally march around the campus in special occasions… that would be really refreshing for midterm weeks. So my first part of the assignment is to simulate the march of the CMU Kiltie Band, from the CFA lawn, via Baker Hall and Porter Hall (for whatever reason), then along the university center outdoor hallway to my study desk in Codelab for a nice private visit. And after realizing midnight is actually perfect for balloon-stabbing activities, I started to run out of balloons to stab. (In fact I intended to record more along the route, but then I decided I’ve scared enough people even in midnight.)

Here are the three IRs, the recording of band practice and the convolution:

As for the other part, I have Deep Water, which used the sound of bubbling water as IR to convolute the sound of windy waving that I accidentally recorded while I was trying to record the sound of my footsteps. And it was followed by three other weird convolutions, Bubbly Baymax, Buggy Baymax and Minion x Minion, which take the voices of the two characters and mix them up.

Here is Deep Water.

And here’s Bubbly Baymax, Buggy Baymax and Minion x Minion:

Time Machine: Starry Starry Night

The idea is to create the effect of a time-lapse photo of a starry night, well, to some point.

There are basically three parts in the patcher. First is the generation of the star trail, and then there is a very slowly moving sky as the background that interact wit the star trail. After combining the two of them together, the third part is using other effects to get a 3*3 effect grid.

I’m using the code structure that we saw during class in the time-shifting lecture about echoing and rotation of video files. I changed some of the parameters and combined two time shifting process together into one video, showing a picture of time-lapse starry night in the sky background.

So first part of my patch deals with the stars. I got some photos with relatively high contrast and resolution, with clean background and nothing else but stars in it. I then turn the photos into still videos that last less than a minute. I made several versions of it and this one works better than the others (sourse: https://jamesrayneau.wordpress.com/2014/10/01/a-quick-guide-to-life/)

a1

Then I found the picture of the sky (sourse: http://free.gatag.net/tag/sky/page/3) on the left of the patcher. Since the sky serves as the background, but I want it to be slightly moving so we can tell that it’s changing along the time.

Sky-Cloud-Blue

And then I used some of the built-in Vizzie modules including Maper, Alphablender, Sketchr, Mutil8r and Foggr to add in some diversity. Unfortunately the computer in the clusters started to froze when I run the code. And here is a general picture of what the whole patcher should look like:

Screen Shot 2015-09-23 at 7.44.39 AM

The computer is time-lapsing every step I move… I’m a little worried it would crush and burn so had to go with the basics eventually. And excuse the buffering in the demo video:

First, the starry part:

(There are four sliders to change the values that could influence the rotation process. And I think it might be more fun to do it manually and see what difference it can make. )

And then there’s the buffffffering one:


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Found Systems: Lost In Translation

I am a ten-year-old English speaker, and can speak Chinese for as long as I can remember. But not until I came to the US 39 days ago did I realize how huge the barrier is between the two languages, when I tried to translate everything for my mom (who cannot speak English) during her time in the US.

Then it occurred to me that some Chinese subtitles for foreign language films can be pretty horrible that they kill some of the best jokes. Things are worse for people who rely on the subtitles to understand where the story goes.

Good translation takes good understanding of both languages, pericise delivery and paraphrasing concerning the context and language habits. And Google Translate is a pretty good substitute if human factors are eliminated.

 

Step One: Find the “Signal”

For starters, I want to find a piece of article that could maximize the influence of my system. It has to be concise and familiar, showing some unique expressions and characteristics of the English language. So I chose the following paragraph from the book The Great Gatsby:

Gatsby believed in the green light, the orgastic future that year by year recedes before us. It eluded us then, but that’s no matter—tomorrow we will run faster, stretch out our arms farther… And then one fine morning— So we beat on, boats against the current, borne back ceaselessly into the past.
― F. Scott Fitzgerald, The Great Gatsby, chapter 9

 

Step Two: Define the “System”

I used Google Translate as the tool and carrier of my system.

The system translates an article written in English into nine other different languages before translated back into English as the output of a whole cycle.

In order to generate a more fun (eccentric) outcome to see what the system can do, I deliberately mashed up the order of the nine languages to avoid more accurate translation within the same language family.

And here is the order of the translation:

English
—1—>Spanish
—2—>German
—3—>Italian
—4—>Danish
—5—>French
—6—>Finnish
—7—>Russian
—8—>Greek
—9—>Chinese
—10—>English

 

Step Three: Go Through the System

In order to be more accurate and efficient during the process (it does require a lot of repetition by hand), I opened ten tabs at the same time in my browser to create a flow and the original article saved in the Evernote.

After setting up both the input and output languages of the ten tabs, the system is all set. I then copied the article from file and pasted in the first input window. And then use the automatically translated output as the input of the next tab… and so forth.

Here is a 40x speed screen recording of the whole process.

As shown in the video, the paragraph went though the system for 15 times, after which I realize the outputs just looping between two versions, one has a word “many” more than the other. So I exited the system. Going through the whole process took me about 20 minutes, and it gave me a slow-motioned version of how Google Translate comprehends and slowly modifies the inputs into a “universal” version of the original article. And it was quite interesting.

 

Post “System” Activities

In order to find certain patterns from the result, I divided each outcome including he original text into three parts with accordingly the same meaning, like this:

USESSSS

 

Then I traced each word and phrase from the original article and marked words or phrases that carry the similar meaning through all the 15 outputs. And the following images show a more colorful version of the outputs:

qqqqqqq

qqqqqqqs

 

After that, I condensed the space between each letter, line and paragraph then rotated the whole text 90 degrees counterclockwise, and it looked like this:

BRWAYGR

 

And then I widened the space between lines and linked up each benchmark in the end of each line into a wavy signal-like diagram. And it looked like this:

huipobuib1

 

These aspects are changed throughout the system. Uncommon words are replaced by common ones, some are changed into synonyms. Some of the phrases changed order, but not so much among longer sentences. Litarature expressions like “borne back ceaselessly”, “orgastic” and “elude” disappeared in the first round (shame). And of course, the final output means nothing like the original, only echoing the words that could bounce inside the Google Translate patterns. And finally it is looping, lost in translation.

 

 

 

*In case you want to know the final looping output:

Green Carnival outside his apartment. Games for (many) years, but I soon realized that this does not mean tomorrow… Tomorrow, crafts and activities, of course, in the last election.

 

And the original:

Gatsby believed in the green light, the orgastic future that year by year recedes before us. It eluded us then, but that’s no matter—tomorrow we will run faster, stretch out our arms farther… And then one fine morning— So we beat on, boats against the current, borne back ceaselessly into the past.