CoroNation Live: S A R R A M -|- Fra Zedde
2021/10/02 @ 9:00 PM
S A R R A M
S A R R A M is an instrumental ambient drone / doom one-man band from Sardinia.
“Albero” is S A R R A M’s fourth record, a mix of drone and ambient soundscapes, crushing and doom-ish guitar soundwalls, very repetitive loops while also incorporating electronic and traditional warm whispers. It’s a very deep and intense soundjourney, a swarm of soundwaves, a dark and dynamic ceremony of frequencies.
Out for Subsound Records May 14th 2021
Listen in Full at sarram.bandcamp.com
Live A/V by:
S A R R A M – guitar, synths, tape loop, mcu, fx
Claudio Spanu – Visuals
Francesco Zedde is a multi-instrumentalist, sound designer, composer, and improviser based in Utrecht. Graduated in electronic music, scoring for films (Pesaro, 2017), sound design (Bologna, 2018) and sound engineering (Bologna, 2019), he has been the member of 13 different projects/bands between 2012 and 2020, has released 6 solo albums, and has taken part to more than 25 records and 475 concerts in 17 countries. Most of his music is released under the monikers “Tacet Tacet Tacet” (live electronics audio/visual project inspired by modern experimental and dark ambient music) and “Tonto”, a post-punk one man band performed with a processed drumset and vocals. Aside from these activities, he is also the founder and organizer of Discomfort Dispatch, a series of free improvisation concerts/ festivals that started in Italy in 2017 and has since taken place in several countries.
Francesco carries his own research activities on augmented instruments, hacking and new media arts while keeping on touring, producing and teaching.
Artificial Intelligence and Art
The study of mechanical or “formal” reasoning began with philosophers and mathematicians in antiquity. The study of mathematical logic led directly to Alan Turing’s theory of computation, which suggested that a machine, by shufing symbols as simple as “0” and “1”, could simulate any conceivable act of mathematical deduction. […] Turing proposed changing the question from whether a machine was intelligent, to “whether or not it is possible for machinery to show intelligent behaviour” .
In its simplest form, artificial intelligence is a field which combines computer science and robust datasets to enable problem-solving. In the last few years, algorithms have become increasingly powerful and available to a broad population of researchers and artists; it is lately possible to observe pictures, music and text realized by softwares that successfully imitate the human artistry to the point that one can believe they are realized by human beings. This development is as charming as frightening depending on one’s standpoint, but its important to note that this still happens by imitation. Machines are just learning better and better how to imitate people behaviour. It is indeed unlikely that a computer will start creating a composition for ambition or intellectual thrust. At best, it only get from a person (the programmer) the tools (algorithms) and the material (dataset) to rearrange a structure of relationships between elements of one or more compositions in order to built a new structure that can trick our perception—or, in other words, display a behaviour that we conceive as “intelligent”. What is the position of an artist in this story? One can argue that, with these developments, the artist can become a programmer, or a programmer can become the artist, using technology as a sort of instrument, using algorithms as composition theories. Indeed, no one can deny that a computer program can be as complex and creative as the Atonal composition theory by Schoenberg or the Well Tempered Tuning system by Bach.
[The video work is performed in real time by a Max/jitter patch of my creation, conceived for live performance.]
Acknowledging that I can use an artificial neural network for creative purposes, I decided over recent years to look into computer theory and experiment with different strategies. By this time my choice has fallen on my favourite childhood game: dismounting toys, messing up with the wiring, and seeing what happens. Though I today call this practice “circuit bending”, “data bending”, creative coding” , “punk attitude” or “noise music”, it is in fact not much more than breaking cheap toys to see what is inside.
A Markov chain is a stochastic model describing a sequence of possible events in which the probability of each event depends only on the state attained in the previous event. A countably infinite sequence, in which the chain moves state at discrete time steps, gives a discrete-time Markov chain (DTMC).
A Markov chain implemented in a basic patch used to be the toy I experimented with before starting composing Artificial Stupidity. Markov chains are particularly interesting when it comes to music making (ask Iannis Xenakis about that!).In simple terms, one can teach to such a patch what are his or her favourite intervals, dynamics, chords and rhythms and then ask the software to make a composition based on his or her musical tastes. The results can be incredibly satisfying if one tweak the parameters correctly, but it will always sound somewhat ‘dumb’ unless the outcome gets edited.
So I have a software that can fairly ape whatever meaningful midi sequence…but what if I feed the wrong data and apply the outcome of the algorithm to the wrong instrument? Of course I get something that sounds really good to me. The largest part of the sounds and sequences in “Artificial Stupidity” are generated by feeding a Markov chain algorithm with short and odd drums grooves; the software gives back midi sequences that I assigned to the cues of a sampler loaded with more drums sounds, or pop songs, weird noises, the Brandenburg concert #5 by Bach.
Intelligence is not always the fundamental need to make music, and foolishness is what makes it interesting sometimes. At lesser, this is a good example of how a computer can perform both intelligent and stupid behaviours depending on how the user uses it; thus, artificial intelligence can be a great tool to make nice music once it sits in creative hands ready for a lot of work.
No input stompbox chain
Detuned pocket radio
Vsts: Glitch II, Battery 4, LABS
Sample sources list:
J.S. Bach – Brandenburg Concert #5, Allegretto (1721)
Edgar Varese – Ionisation (1933)
Sergei Prokofev – Sonata no.6, Op 82: II Allegretto (1940)
Velly Joonas – Stopp, Seisku Aeg! (1983)
36 – Hypersona (2009)
Mùm – Toothweels (2013)