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.

visualizing-looming-attacks

Visualizing looming attacks

Stefano Fasciani

Max is used to analyze voice signal, computing a large set of low-level vocal features, to analyze the instrument parameter-to-sound relationship, computing a set of perceptual timbre descriptors. Vectors and matrices are handled in Max with the FTM extensions. This data is used to compute outside Max the mapping with unsupervised machine learning techniques.

Max is then used to implement part of the mapping in real-time, and to provide the user with an extensive GUI for tuning at runtime mapping algorithm, and it communicate via OSC with the VCI4DMI core routine.

A set of Max For Live devices allow the communication between the VCI4DMI and Ableton Live, which host the instrument (native or plugins), for analysis or real-time control purposes.

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