jrubisch

iMAGE is a graphics-based synth based on basis function graphs that are scanned with two independent oscillators that are running in X and Y direction.


You can also modulate the generated image size with an LFO, and add visual feedback to avoid audible artifacts and allow for smooth timbral transitions.


Listen to the examples for the richness and variety of available sounds!



iMAGE is a graphics-based synth based on basis function graphs that are scanned with two independent oscillators that are running in X and Y

image

iMAGE

tmhglnd

WARNING: This video may potentially trigger seizures for people with photosensitive epilepsy.

"The digital world is consuming a bigger and bigger part of our lives and the fear of missing out is getting stronger. The human race isn't capable to restrain herself and gets flooded by the stream of digital information. There is only one solution, locking down the cyber sea."

In this work I succeeded in constraining the sea of digital information. But it does not like to be locked up in a digital cage. Hear and see it struggle to break free, but eventually gives in and loose the fight.

This is an audio-visual composition entirely programmed in Max7 with Jitter and OpenGL. Manipulating the jit.bfg in time to generate a matrix that is post processed by various effects such as brightness and contrast. This matrix is used as amplitude values for an FFT. The input for the FFT is noise combined with a ring-modulated subtractive synthesis of a saw and frequencyshifted rectangle wave.

WARNING: This video may potentially trigger seizures for people with photosensitive epilepsy.
"The digital world is consuming a bigger and b

cybersea-lockdown

CyberSea LockDown

Andrew Benson

It’s a slow month in the Package Manager, so I wanted to take the opportunity to share with you a hidden treasure tucked away inside the jit.mo package. While jit.mo is billed primarily as a motion graphics toolkit, there’s one object in there that every Max user could probably appreciate: jit.mo.time.

Since we released jit.mo toolkit, this object has become one of those objects that ends up in every single patch I make, even the ones that don’t involve wiggling shapes. The fact that it automatically syncs its output to a jit.world context certainly helps, but it doesn’t prevent you from driving it with other means. To use it without jit.world, just add “@automatic 0” and provide it with a steady supply of bangs.

The most widely useful feature of jit.mo.time is its “function” mode, which turns it into a message-rate LFO with saw, sine, triangle, line, and perlin noise waveshapes. This makes it an efficient drop-in replacement for all those cycle~ + snapshot~ instances you might have scattered around your patches. The timing of the LFO is clocked to real time, so even if your bangs have some jitter to them you will get a steady timed value out.

The “accum” mode of jit.mo.time gives you a running-time clock value that can be used to drive the rest of your patch. This is super useful when you are running a show that relies on tight time sync, and for driving animations and simulations that evolve over time.

Finally, the “delta” mode gives you the time in between frames (or bangs, if not running with jit.world). This can be used as a time multiplier, for example, when doing particle or physics type simulations where variations in frame timing could ruin the effect. It can also come in handy if you just need to know how long it has been since the last event.

I encourage you to install the jit.mo package from the Max 7 Package Manager and  give jit.mo.time a try and see if it solves any of your patching problems. 

Screen Shot 2018-03-26 at 7.29.01 AM.png

A relative lull in Package Manager activity this month gives me a golden opportunity to share a hidden treasure with you tucked away inside the jit.mo package: The jit.mo.time object. In its “function” mode, it provides an efficient drop-in replacement for all those cycle~ and snapshot~ instances you might have scattered around your patches.

content-you-need-jit·mo·time

Content You Need: jit.mo.time

matmat

ARTECHOUSE, a new museum in Washington DC specialising in digital art, asked us for an interactive installation for September 2017. 

We created Spirit Of Autumn, an interactive and participative installation based on the autumn theme. 
Visitors took part in the experience by customising an autumn leaf on paper. Digitalised, it appeared in real time in the wind of the autumn landscape before being integrated into one of the five interactive stations: the leaf ballet, the pile of leaves, the body of leaves, rain and thunder.
In one month, more than 15 000 leaves were coloured.

Conception, development, interaction: A-BLOK
Design: A-BLOK & ARTECHOUSE
Max MSP main developer: Mathieu Constans
Body of leaves: Guillaume Bertrand
Wind  developer: Federico Foderaro
Video setup: Silva Sveta

ARTECHOUSE, a new museum in Washington DC specialising in digital art, asked us for an interactive installation for September 2017. 

We cre

spirit-of-autumn

Spirit Of Autumn

Evgeniy Vaschenko

The artist creates a space as a mind's dream. His brain waves are controlling the code of a robotic composition to create an abstract space in the hotel room. He meditates on dreaming and the resulting changes of his brainwave frequencies will transform the code. By that he builds his artistic spaces – an abstract visual 3D world and sound scape. So his inner state, similar to a lucid dreaming mind, will be projected into the external physical world of the hotel room. Visitors are invited to take over the artists role of creating the space with their own dreaming minds.  

The interface technically reacts on raw dinamic of the nerosky mindwave sensor. The datastream is interpreted by the v4w.enko algorithms in 3d surf implementation and reparamentrisation with mindwave datastream. Sound is generated with changing frequency from 20 to 20000Hz according and scaled to raw mindwave nerosky datastream with synchronisation to v4w.enko algorithmic structure of mindwave reparametrisation. 


The artist creates a space as a mind's dream. His brain waves are controlling the code of a robotic composition to create an abstract space i

in«ter»sect

in[ter]sect

Duffield

Panoptidrome is an interactive audio-video surveillance sculpture that tracks the faces of people in a space and creates a 3-D scan of them. The scan is then multiplied and videomapped onto square tiles on the wall. The piece was created with Max 7, Kinect V2 sensor, and security mirrors. The Kinect V2 was hooked up to Max 7 and used Dale Phurrough‘s dp.kinect2 external object.

Presented at Videodrome 2015 at the Museum of Contemporary Canadian Art, Toronto, Canada.

panoptidrome

Panoptidrome

karl krach

As we believe that time and space are inextricably connected, we commonly perceive the progression of time as motion - either by the motion of our surroundings or by our own movement in space. The interactive video installation ‘Der Cryptochron’ explores this hard-wired time-space link in our brains by breaking this connection and splitting apart our motion from the motion of our surroundings. It creates a realtime window into a non-real (spatially controlled) time.

Done in Max/Jitter. Externals used: dp.kinect2, f0.ultimate_counter.js and dmxusbpro. Special thanks to Dale (dp.kinect) and Fredrik (f0.ext) for developing these externals and their really amazing support!

der-cryptochron

Der Cryptochron

China Blue Wong

MindDraw by China Blue in collaboration with Christopher Konopka and neuroscientist Seth Horowitz is a pioneering real-time interactive viewer participation work that enables people to create beautiful brain based images. Through the usage of an EEG data sensor brain waves are converted into stunning constantly fluxing images and sound. To engage the work, the participant  sits near the projection and simply dons the brainwave sensor headset. With the headset in place MindDraw enables the user to look into their own mind and see their internal world reflected in dynamic externalized patterns. And, although the brain wave frequencies monitor the conditions of attention, awareness, relaxation, sleep, and cognition, MindDraw shows how all of these elements emerge and interact in an intricate dance. 

With  MindDraw we are extracting one of nature’s most complex patterns, this is more than just a simple light and sound summation of brainwave frequencies but the epiphenomenon of the mind. 

MindDraw by China Blue is a pioneering real-time interactive viewer participation work that enables people to create beautiful brain based images.

minddraw

MindDraw

MikeR77

Video Voto Matic combines a drum sequencer and a vintage voting booth. It samples footage from the current presidential race and allows a user to sequence several individual video tracks on a sixteen-beat loop. Users punch their rhythm into a “voting booklet” that mimics the infamous punchcard-style Votomatic voting machines used during the 2000 Florida election debacle. Instead of a providing the names of candidates, the booklet is filled with sample drum patterns. Users can follow the patterns suggested by the booklet or they can punch in their own. Several stations exist, and an installation would closely resemble a real-life polling station, resulting in an uncanny experience for the participant. 

The entire interface is programed in Max, which allows for precise timing. The video clips are handled with jit.gl objects allowing for quick video processing.  The aesthetics of the interface owe a great deal to the Max programming environment.

Users punch their rhythm into a “voting booklet” that mimics the infamous punchcard-style Votomatic voting machines used during the 2000 Florida election debacle. 

video-voto-matic

Video Voto Matic

jayhardesty

This algorithm focuses on when to generate notes, based on analyses of syncopation and repetition within Live MIDI clips. As the algorithm morphs between inputs, transition tables are continually updated with probabilities of next attack times. The application is written in Swift, communicating via Max-for-Live and OSC.

Two types of visualization are implemented in Jitter:

1) On a Max-for-Live device are two graphs, each showing probabilities for the most probable next attacks on the X-axis, based on the current attack shown on the Y-axis. 

2) A separate video window continually jumps to a collection of future video frames. Each future frame is crossfaded according to the probability of the attack time coinciding with that frame. A second video window shows time unfolding normally, catching up with the frame shown to the left just as each attack is generated.

These visual components are intended not so much as artistic output, but as stabs at better intuiting the algorithm.

Search Results