Revision as of 15:56, 16 May 2022

.my notebook is the place where everything is // i like to annotate in there what get stuck into my brain for one reason or another (//not all the thoughts are my exclusive property)

.special issue 18 // 12.04.22_

12.04.22

.present ourselves through relations

There is a city in the south of Italy, where the water is really blue and you can actualy see the bottom.There is a city in the North, that is Venice, where you can't see the bottom. There is a city in the UK where the water can be transparent but ... ocean, you can see a different sound. There is a city in the NL (Rotterdam) where sometimes you can see something, sometimes you can... what you mostly see are reflections on the water rather than underneath.

.analysis: There were no human actors in this one, factual statements, connected by materiality (water) describing them in a factual way, rather than emotional. Because it comes from Emma's mouth, I can see what Emma is interested in, and when she navigates cities, what she focusses on. A perspective interest. Between the factual and observational, repetition of "there is a city", "you can see"... Paints the scene and introduces someone that is both speaking and drawing you to that city. Both making the city emerge and also introducting the observer of thoses different waters in those different cities. By layering we start to understand that this much be a observer travelling. Destiny doesn't seem to be in this one. No Teleology here or in previous... Bringing non inter-personal relationships, also happening here , the city and the water takes part in what makes an introduction.

.how to make a microphone:

19.04.22

our first release: week 01

audio levels in audacity, transcoding & compressology, 4 effects to edit audios: loop, stretch, reverse, cut

20.04.22

steve's problems of notation: https://pad.xpub.nl/p/Problemsofnotation-for_annotation

texts: Text by Simon Yuill https://www.metamute.org/editorial/articles/all-problems-notation-will-be-solved-masses; Scratch Orchestra's Nature Study Notes (1969) http://intuitivemusic.dk/iima/sonsn.pdf and John Cage's Song Books Vol 1 (1970)

https://pad.xpub.nl/p/Problemsofnotation-for_annotation-MIRIAM&EMMA

emm and miri's notations

Start a rap battle and take turns. You're only allowed to say types of food/drinks you were not allowed to consume as a kid. Example: "Red Bull" - "Oreo cookies" - "Nutella" Chose a partner and an object. The item needs to be squeezed with the left hand of person 1 and the right hand of person 2. Take an existing song lyric and write it down. Try syncing the rhythm of writing with the rhythm of the song without listening to the actual song. Imagine you're a soccer coach and you need to scream over the field to give your team advice but you're not allowed to use language. Imitate the sound of your evening routine by scratching sounds on a table. Imagine you are Jeff Bezos. Start playing. Imagine you are Nelson Mandela. Start playing. Imagine you are Angela Merkel. Start playing. Imagine you're the owner of a café and your coffee machine just broke down. You try imitating the sounds of a coffee machine so the customers won't notice that the coffee machine broke. Think of an embarrassing thing that happened to you. Think about where exactly you feel that feeling of embarrassment in your body. Make sounds with these body parts only. 11. One goes underneath a table and puts their face on the surface. One goes on the top of a table. Both whisper to each other with their face on the surface. 12. Look at a scar you have. Try playing an instrument with that scar only. 13. Imagine you are the sun. Start playing. 14. Imagine the sounds you make while sleeping. Play them. 15. Try to play an instrument the way a giraffe would do it. 16. Scream "Fuck!" after every note you play. 17. Hum the sound of a commercial jingle you know but with your mouth open. 18. Calculate how many days it takes for your birthday. Take the nearest book you find, open page number [days until birthday] and the first noun you read will be your next instrument. 19. Eat a meal in the sun. 20. Think of a question. Look for the answer on wiki how. Follow the instructions. Only read words that start with the first letter of your mother's name. 21. Think of a rhythm. Don't play it. Neither move. 22. Stare at the sun without sunglasses. Describe the feeling in a calm and juicy way. 23. Imagine you are your alarm clock. Sing "We will rock you". 24. Take your shoe off and hold the opening of the shoe over your mouth. Start singing. 25. Go to a coffee machine. Order 2 coffees. Drink them in the sun. 26. Go into a big room. Make a sound. Go into a smaller room. Make a sound. Go into a smaller room. Make a sound. Go into a smaller room. Make a sound. Go into a smaller room. Make a sound. 27. Think of a melody. Ask someone on the left to think of a melody. Start singing the melodies simultaneously. 28. Say out loud something you would whisper. Whisper something you would say out loud. 29. Sit somewhere. Shuffle a deck of imaginary cards. Distribute them to the table and read the future. 30. Pick a song you like but it's not well known. Make another person listen to it. Make the other person sing the song without listening to the song again. 31. Sit on a chair. Fold your body and put your head upside down, in the middle of your knees. Try to look backwards (with your head towards the floor). Tell a story of your childhood. 32. Exchange your shoes with someone. Walk backwards.

our notations

25.04.22

experimenting with a piano piece in audacity.. (reverb, paulstretch)

https://hub.xpub.nl/soupboat/~flem/pianoexp_WEEK2.mp3

the terminal and its commands

plotter pen

26.04.22

on the topic of uneven patterns: Week 02

https://en.wikipedia.org/wiki/Instructional_theory

https://en.wikipedia.org/wiki/Instructional_materials

https://en.wikipedia.org/wiki/Instructional_design

http://tattfoo.com/discovery/Instructional.html

on diffraction and reflection

09.05.22

why not use VOSK to do live printing with the line printer?

vosk python3 script.py >> filename.txt
>> (to append)
> (to overwrite)
how to talk to the printer? same concept that creating txt file

10.05.22

on the topic of emergent opera: Week 3

sound experiments: soundsexperiments

https://monoskop.org/reader/ --> project comments pad

with Kim&Jian

12 Inverse Reader Dušan Barok, monoskop.org The Inverse Reader is a collection of 64 writings, talks and conversations about shadow, independent and artists’ digital libraries. While they are associated mainly with questioning of intellectual property and struggle for access to scholarly communication and artistic expression, communities around these libraries have also been actively engaging with amateur librarianship, scholar-led publishing, the politics of search, pirate care, critical pedagogy, self-education and other things which are brought here together. The reader contains a growing selection of more than sixty statements and texts presented at gatherings and publications over the past ten years. It is presented as a collective index of words and expressions from across the corpus. The terms are selected (semi-)automatically using a “tf-idf” algorithm [1] and linked to passages in the texts. The interface allows for adjusting the number of displayed terms and controlling the display of personal names. The list of all included texts is at the bottom (with controls to include, exclude and display the given text). The reader has been created on the occasion of the exhibition at Panke.Gallery and is also available online at https://monoskop. org/reader. Visit https://monoskop.org/Digital_libraries for more.

https://creatingcommons.zhdk.ch/reader-on-shadow-artistic-independent-autonomous-digital-libraries/ https://en.wikipedia.org/wiki/Tf%E2%80%93idf

https://monkeylearn.com/blog/what-is-tf-idf/#:~:text=TF%2DIDF%20(term%20frequency%2D,across%20a%20set%20of%20documents.

tf-idf
term frequency of a word in a document x inverse document frequency
Term frequency
Suppose we have a set of English text documents and wish to rank them by which document is more relevant to the query, "the brown cow". A simple way to start out is by eliminating documents that do not contain all three words "the", "brown", and "cow", but this still leaves many documents. To further distinguish them, we might count the number of times each term occurs in each document; the number of times a term occurs in a document is called its term frequency. However, in the case where the length of documents varies greatly, adjustments are often made (see definition below). The first form of term weighting is due to Hans Peter Luhn (1957) which may be summarized as:[3] The weight of a term that occurs in a document is simply proportional to the term frequency.
Inverse document frequency
Because the term "the" is so common, term frequency will tend to incorrectly emphasize documents which happen to use the word "the" more frequently, without giving enough weight to the more meaningful terms "brown" and "cow". The term "the" is not a good keyword to distinguish relevant and non-relevant documents and terms, unlike the less-common words "brown" and "cow". Hence, an inverse document frequency factor is incorporated which diminishes the weight of terms that occur very frequently in the document set and increases the weight of terms that occur rarely. Karen Spärck Jones (1972) conceived a statistical interpretation of term-specificity called Inverse Document Frequency (idf), which became a cornerstone of term weighting:[4] The specificity of a term can be quantified as an inverse function of the number of documents in which it occurs.

reading of saidiya hartman - the plot of her undoing

16.05.22

vcv rACK 2: substractive synthesis https://vcvrack.com/Rack

why not using this for bassline of the creative writing pieces?

audio experiment

presenting instruments: complex synthetic voice, a highlighted reading session

using the tf-idf algorithm to produce sound --> the more the word is present in the text the louder it is

every word has their own weight

purpose: interpret a text/reading a text with a synthesised voice that doesn't try to be human but follows some specific words to pronounce words (pitch, volume, speed..etc)

to do:

synthetic voice

text to work with: a set of texts in the same language -->>>>>>> rejection letters?

algorithm history: to get the more significant choice, understand how it works, decide consciously how to filter and pronounce

can I work on a translation instrument? apfel>sound>apple>mela>sound

ideographic notation =/ sound notation

tfidf + synthetic voice

synthetic voice: espeak and adjust volume/voice/rate/[pitch? ] "espeak -a 100 'hello'" is very loud "espeak -a 50 'hello'" is only half very loud

TFIDF history Karen Spärck Jones designed (part of) the algorithm

https://www.askpython.com/python/examples/tf-idf-model-from-scratch https://www.nytimes.com/2019/01/02/obituaries/karen-sparck-jones-overlooked.html https://www.historyofdatascience.com/karen-sparck-jones-the-search-engineer-enabler/

17.05.22

on the topic of jingleboard parliament: week 04

User:FLEM/1.3: Difference between revisions