eShofar2: A chaotic performance system for electronically expanded ram's horn, with conditional performer influence (2005)

2005 Bob Gluck

Sound clips:

Three streams of shofar sounds are recorded in real-time, each processed using a hi-pass filter.

Each stream is digitally processed with hi-pass, low-pass and comb filters and a series of harmonizer algorithms and a cluster of time stretch and multi-tap delays.

The discrete parameters governing the algorithms are not subject to direct control by the performer.

Changes in each parameter are governed by one of ten pre-recorded graphs of values unfolding on a timeline.

The assignment of a particular graph to a specific parameter is governed by a random decision, which periodically changes.

The rate of change of parameter values is related to the analysis of the degree of noise elements in the performed shofar sounds. A noise quotient is assessed using an analysis algorithm designed by Tristan Jehan as a Max/MSP object called analyzer~. More noise, i.e. breathiness, slows down the pace of the timeline.

A sound "intensity" rating (average pitch plus one half of a loudness factor) is randomly assigned to a discrete processor parameter or a cluster of parameters. Loud and high pitched = "intense".

Thus the performer has limited influence on what processor is being influenced by the real-time performance gesture.

Screenshot 1 of the main Max/MSP eShofar2 patch:

right side:
Intensity parameters are tracked

values currently mapped to particular processing algorithms

Screenshot 2 interactivity subpatch:

bottom left: the two main performance gestures
that trigger global changes
can be viewed and sent to their destinations.

Bottom right: performance features that are tracked
are clustered to determine intensity values
and sent to their respective
control algorithm destinations.

Screenshot 3 subpatch:

one of the pre-drawn graphs randomly assigned to parameters of a processing algorithm.

Randomly assigned sets of values are sent to their current respective processing algorithms.

Algorithm assigns graphs to particular processing parameters.

Assignments are sent to their respective destinations.

Screenshot 4:
Interval tracker subpatch

The performer can toggle on an algorithm that tracks
repeated notes and perfect fifths,
both common traditional shofar sound gestures,
which may influence the system.

Rapidly repeated notes:
can freeze the movement of the timelines
so that all values remain at a constant state for six seconds.

Perfect fifths can reset all parameter values and change
the assignment of value graphs to processor parameters.