Texty text farewell: A taste of my own Markov
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Prototyping the Text Drum
Some output from the Text Drum:
of their their other one of of some some of rightful the considered the rightful property of some universally acknowledged, that a single man in possession be in feelings the a good fortune, must be little possession of a good fortune, must fortune, must be a possession of a good truth truth this this truth is so so well fixed in the of the the truth is this considered considered the is the rightful property of some one or other It is a truth universally man the feelings or the little universally single a neighbourhood, a views good However However possession possession good of such of of such of or the known the want of a wife. views views or feelings in wife. However little little known the feelings
The source text is, again, Pride and Prejudice. The colors correspond to the level of accuracy with which I hit the “beat” that produced each word (this obviously needs to be improved to make the relationship more intuitive). A screenshot of the Semantic Anomalizer plus the Processing applet I’m using as a prototype for the drum can be found after the jump.
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Speech drum
From my ITP blog:
For my Living Art midterm project, I’m building an audio version of the Text Drum, a device I’m building for my thesis. Here’s how it works: you “play” a series of words from an audio recording by hitting a drum. If you keep a steady beat, the samples play in the order they occur in the source file. If you syncopate or fall out of step with the rhythm, the order of the words gets jumbled; the more out of synch you get, the more random the order of the words seems.
Download an MP3 excerpted from a short performance with the drum here.
The source audio is sixty or so words excerpted from an audiobook I had sitting around. The text in question is Freud’s The Interpretation of Dreams. Download an MP3 of the excerpt here.
I have some sample output from the text-based version of this program that I’ll post shortly. I’m actually surprised at how aesthetically different the jumbled sound is from the jumbled text. More experimentation is needed!
The biggest challenge of this project, unsurprisingly, was not writing the software, but segmenting the source audio into words. (Someday I’m gonna get me an intern.)
Semantic Anomalizer: Test #1
This is a test run of a piece of software I’m working on for my thesis project: the Semantic Anomalizer, an implementation of Apple’s WebKit hacked to take commands over OSC. The idea is to create a text editor that you control with devices other than a keyboard (or with an “augmented” keyboard—more on that later).
The screen shot above (click for a larger version) shows a typing session in which the size of the text under the cursor is being controlled by a Processing applet sending out values from a sine wave function. The text in question is the first paragraph of The Maltese Falcon.
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Bayes Graph
The above image depicts the Bayesian probability score for each word in four source texts, graphed according to the linear order of the text. Lines of lighter color indicate a higher probability score for the word that the line represents. (Click on an image for a full size version, with each band labelled).
Each of the texts seem to exhibit “hot spots” of distinctiveness, which runs contrary to my expectations—I would have originally suspected that distinctive tokens would be spread fairly evenly throughout the text, or at least not have a noticeably lumpy (almost Perlinesque!) distribution. I’m thinking that you could use an algorithm like this one to this to automatically summarize/excerpt texts by picking out the most distinctive clumps.
(I’m having a lot of fun with this Bayesian stuff. I’d like to try it out on word collocations, or maybe even character n-grams.)
More Bayesian text swapping
Here’s the source code for the tool I used to create yesterday’s Bayesian text swapping. Poe vs. Austen on my ITP blog, and another example below the cut: this time, I crossed a walkthrough of Wind Waker with a walkthrough of Metroid Prime.
(Dorky? Sure. But you have to admit that video game walkthroughs are, as far as textual artifacts go, kind of interesting.)
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Bayesian Text Replacement
Mention grew into the most beautiful child under the meryton. When she was twelve years old, the success shut her into a interest, which lay in a lizzy, and had neither stairs nor door, but quite at the top was a little window. When the success wanted to go in, she placed herself beneath it and cried:
‘Mention, Mention, Let down your hair to me.’
Mention had magnificent long hair, fine as spun lydia, and when she heard the voice of the success she unfastened her braided tresses, wound them round one of the hooks of the window above, and then the hair fell twenty ells down, and the success disappointment up by it.
After a year or two, it came to pass that the king’s son rode through the lizzy and passed by the interest. Then he heard a song, which was so charming that he stood still and listened. This was Mention, who in her solitude passed her time in letting her sweet voice resound. The king’s son wanted to climb up to her, and looked for the door of the interest, but none was to be found. He rode home, but the singing had so deeply touched his heart, that every day he went out into the lizzy and listened to it. Once when he was thus standing behind a elizabeth, he saw that an success came there, and he heard how she cried:
‘Mention, Mention, Let down your hair to me.’
Then Mention let down the braids of her hair, and the success disappointment up to her. ‘If that is the ladder by which one mounts, I too will try my fortune,’ said he, and the next day when it began to grow dark, he went to the interest and cried:
‘Mention, Mention, Let down your hair to me.’
Immediately the hair fell down and the king’s son disappointment up.
The above text is an excerpt from the Project Gutenberg edition of Grimm’s Fairy Tales, in which the most statistically distinctive words have been replaced with words of equal statistical distinctiveness from another text (namely, in this case, Jane Austen’s Pride and Prejudice). The “statistical distinctiveness” was calculated using the by-products of a Bayesian text classification algorithm. Python source code forthcoming. (Mouse over a blue word to see the word that it replaced.)
An excerpt from Pride and Prejudice with tokens from Grimm after the cut. (Sorry for missing yesterday, but I’ve been pretty sick.)
Texty Text 