User:Jacopo/Radioimplicancies: Difference between revisions

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The log of the number of documents divided by the number of documents that contain the word w. Inverse data frequency determines the weight of rare words across all documents in the corpus.
The log of the number of documents divided by the number of documents that contain the word w. Inverse data frequency determines the weight of rare words across all documents in the corpus.


[[File:Ti-idf-try.png|500px|center]]
[[File:Ti-idf-try.png|1080px|center]]


= Setup Python on terminal (Mac) =
= Setup Python on terminal (Mac) =

Revision as of 16:39, 8 December 2021

Treatise by Cornelius Cardew

Radio Implicancies

Frequencies2.jpeg

If technological systems are implicated in the structuring of knowledge and knowledge systems are implicated in how technology operates … how do we start to think the world otherwise?!

Every Thursday afternoons, Radio Implicancies broadcasts from 16:00-17:00

Basic-radio-graph.png


These public broadcasts are ongoing experiments with a specific subset of technological tools for sharing and formatting knowledge. Radio Implicancies will use any means necessary i.e. different protocols and editorial approaches: audio streaming, live-on-tape, DJ-ing, on-line reading groups, web-rtc, liquid soap, podcasts, xmpp chat, …

Spacing in Time

Clock.jpg

Week 3.
In a system dominated by measures, demanding an ever increasing synchronization between individuals and their environments, we propose "Spacing in Time" as a way to escape per-determined variables. Through these audio experiments, we encounter a new moment of experiencing different time flows. The radio turns into a time's perception box where various tools for time measurement and orientation are being de-fragmented and re-boot. While creating audio environments with sound repetitions, reverbs and echoes, the experiments lead to a sense of physicality which allows the full immersion into a broadcasted, time travelling. Shut down the clocks and de-synchronize yourself from the established recognition of time, while un-practicing your common perceptions.
I collected some audio-experiments here.

The Word of Unbinding

by Ursula K. Le Guin

Pure Data

Pd is a real-time graphical programming environment for audio and graphical processing. It resembles the Max/MSP system but is much simpler and more portable; also Pd has two features not (yet) showing up in Max/MSP: first, via Mark Dank's GEM package, Pd can be used for simultaneous computer animation and computer audio. Second, an experimental facility is provided for defining and accessing data structures.

Pd can easily work over local and remote networks to integrate wearable technology, motor systems, lighting rigs, and other equipment. It is suitable for learning basic multimedia processing and visual programming methods as well as for realizing complex systems for large-scale projects.

Algorithmic functions are represented in Pd by visual boxes called objects placed within a patching window called a canvas. Data flow between objects are achieved through visual connections called patch cords. Each object performs a specific task, which can vary in complexity from very low-level mathematical operations to complicated audio or video functions such as reverberation, FFT transformations, or video decoding. Objects include core Pd vanilla objects, external objects or externals (Pd objects compiled from C or C++), and abstractions (Pd patches loaded as objects).

Algorithmic functions are represented in Pd by visual boxes called objects placed within a patching window called a canvas. Data flow between objects are achieved through visual connections called patch cords. Each object performs a specific task, which can vary in complexity from very low-level mathematical operations to complicated audio or video functions such as reverberation, FFT transformations, or video decoding.

Documentation

Configuration

With Liquidsoap (Terminal)

  • playlist.liq
  • playlist.m3u (txt)
  • Terminal (liquidsoap)

Liquidsoap Cookbook: https://www.liquidsoap.info/doc-1.4.4/cookbook.html

Schermata 2021-04-14 alle 18.10.39.png

First configuration: https://hub.xpub.nl/sandbot/SI15/radio.html

  • playlist.liq:
set("log.file",false)
set("log.stdout",true)
# playlist can be changed to other commands
myradio = audio_to_stereo(playlist("playlist.m3u"))

%include "/srv/radio/passwords.liq"

output.icecast(%vorbis,
     host = ICECAST_SERVER_HOST, port = ICECAST_SERVER_PORT,
     password = ICECAST_SERVER_PASSWORD, mount = "myradio.ogg",
     mksafe(myradio))
  • playlist.m3u (txt):
file1.mp3
file2.mp3
file3.mp3
...
  • on Terminal:
liquidsoap playlist.liq

Re-reading a text - Recording Test

Un-Naming the essay with NLTK

import nltk

text = open('./nameless.txt').read()
tokens = nltk.word_tokenize(text)

tagged = nltk.pos_tag(tokens)

selection = []

for word, tag in tagged:
    if 'NNS' in tag:
        selection.append(word)
    if 'NN' in tag:
        selection.append(word)
    if 'NNP' in tag:
        selection.append(word)
    if 'NNPS' in tag:
        selection.append(word)

print(selection)

['namelessness', 'indifference', 'names', 'names', 'Whales', 'Whales', 'dolphins', 'dolphins', 'seals', 'seals', 'sea', 'otters', 'otters', 'alacrity', 'anonymity', 'element', 'faction', 'yaks', 'yaks', 'yak', 'everyone', 'creatures', 'creatures', 'rats', 'rats', 'fleas', 'fleas', 'hundreds', 'hundreds', 'thousands', 'thousands', 'names', 'names', 'Babel', 'Babel', 'yaks', 'yaks', 'name', 'matter', 'summer', 'councils', 'councils', 'females', 'females', 'name', 'others', 'others', 'point', 'view', 'argument', 'light', 'bulls', 'bulls', 'consensus', 'onset', 'blizzards', 'blizzards', 'beginning', 'thaw', 'agreement', 'designation', 'yak', 'donor', 'animals', 'animals', 'anybody', 'failure', 'Dean', 'Dean', 'Swift', 'Swift', 'attempt', 'vocabulary', 'Cattle', 'Cattle', 'sheep', 'swine', 'asses', 'asses', 'mules', 'mules', 'goats', 'goats', 'chickens', 'chickens', 'turkeys', 'turkeys', 'names', 'names', 'people', 'people', 'couple', 'problems', 'problems', 'pets', 'pets', 'cats', 'cats', 'course', 'name', 'names', 'names', 'poet', 'Eliot', 'Eliot', 'hours', 'hours', 'none', 'contemplators', 'contemplators', 'names', 'names', 'onlookers', 'onlookers', 'object', 'gaze', 'fact', 'Perfect', 'Perfect', 'Platonic', 'Platonic', 'Mouse', 'Mouse', 'case', 'point', 'dogs', 'dogs', 'parrots', 'parrots', 'lovebirds', 'lovebirds', 'ravens', 'ravens', 'mynahs', 'trouble', 'arose', 'individuals', 'individuals', 'names', 'names', 'part', 'issue', 'choice', 'anybody', 'Rover', 'Rover', 'Froufrou', 'Froufrou', 'Polly', 'Polly', 'Birdie', 'Birdie', 'sense', 'objection', 'lowercase', 'creatures', 'creatures', 'uppercase', 'appellations', 'appellations', 'parrot', 'dog', 'bird', 'Linnaean', 'Linnaean', 'qualifiers', 'qualifiers', 'years', 'years', 'tin', 'cans', 'cans', 'tail', 'insects', 'insects', 'names', 'names', 'clouds', 'clouds', 'swarms', 'swarms', 'syllables', 'syllables', 'fish', 'sea', 'names', 'names', 'silence', 'oceans', 'oceans', 'faint', 'blurs', 'ink', 'currents', 'currents', 'trace', 'None', 'way', 'skin', 'night', 'while', 'day', 'names', 'names', 'barrier', 'fear', 'fear', 'fear', 'attraction', 'desire', 'feel', 'rub', 'caress', '’', 's', 'scales', 'scales', 'skin', 'feathers', 'feathers', 'fur', 'taste', '’', 's', 'blood', 'flesh', 'warm', 'attraction', 'fear', 'hunter', 'eater', 'food', 'effect', 'conscience', 'exception', 'myself', 'Adam', 'Adam', 'father', 'lent', 'gift', 'peevish', 'impression', 'attention', 'O.K.', 'O.K.', 'reasons', 'reasons', 'talk', 'let', 'decision', 'things', 'things', 'notice', 'anything', 'goodbye', 'garden', 'key', 'parts', 'parts', 'O.K.', 'O.K.', 'fine', 'dear', 's', 'dinner', 'the—', 'fact', 'words', 'words', 'steps', 'steps', 'path', 'house', 'dancers', 'dancers', 'winter', 'shining']

Tf-Idf

Term Frequency (TF): The number of times a word appears in a document divded by the total number of words in the document. Every document has its own term frequency.

Inverse Data Frequency (IDF): The log of the number of documents divided by the number of documents that contain the word w. Inverse data frequency determines the weight of rare words across all documents in the corpus.

Ti-idf-try.png

Setup Python on terminal (Mac)

install brew

/bin/bash -c "$(curl -fsSL 
https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
brew install python3
pip3 install NLTK

The Hackchat

Hackchat is a minimal, distraction-free chat application. Channels are created, joined and shared with the url, create your own channel by changing the text after the question mark. If you wanted your channel name to be 'your-channel’: https://hack.chat/?your-channel There are no channel lists, so a secret channel name can be used for private discussions.

Sonic Pi

Follow the tutorial: https://sonic-pi.net/tutorial.html

Pads

Summary

15.1

15.2

15.3

15.4

15.5

15.6

15.7


15.8