Download - CCRMA - Stanford University

fingering may be. Further, these instruments provide no intermediate finger position information
for ringer states bUween open and closed.
The solution was to take a Yamaha MIDI wind controller fW'Xl 11 and place Force Sensing Resistors
iFSRs) under each key to determine the key positions. Between the FSRs and the key. a small
piece of foam was inserted. In this way. the FSR was driven in its initial highly nonlinear range.
Each FSR was connected via ribbon cable to a BASIC Stamp II microcontroller, which was used
to determine the key position and output the result in the form of MIDI ControlChange messages.
Because I an; also using breath pressure MIDI messages from the WXll. I merge the two MIDI
channels before inputing the result to my STK instrument. For more information on the Holey
Controller, see http: //www-cenna. Stanford. edu/~gary/
• The Phoney Controller (aka "The Air Phone")
The Phoney Controller consists of a telephone housing, four Force Sensing Resistors (FSRs). an
Analog Devices ADXL202 two-dimensional accelerometer. a BASIC Stamp II (BSII) microcontroller,
a stupid "on-off" switch, and a cool LED! I am essentially using the BSII as a MIDI
sequencer. All sounds at this point have to be generated from an external MIDI synthesizer. The
FSRs and the 2D accelerometer are used to control various aspects of the sequencer.
The Phoney Controller was built in part to serve as a "one-man band" at my wedding. But
mostly, is was built for goofing around and having fun. Given the memory limitations of the BSII.
sequences have to be pretty short. That's the challenge ... coming up with simple but interesting
patches that someone will enjoy improvising with for hours. For more information on the Phoney
Controller, see http://www-ccrma.stanford.edu/~gary/
6.4 Psychoacoustics and Cognitive Psychology
6.4.1 Neural Network Models of Musical Cognitive Activities
Jonathan Berger. Daniel Lehmann. and Dan Gang
Artificial neural networks provide a flexible environment within which we model the mechanics and
implied associated cognitive processes involved in human prediction of time ordered sequential musical
elements. We model an experientially trained listener's cognition of functional tonal western music. By
interpreting the distribution of output activations of the network as expectations for the next event in
the sequence and comparing this to the consequential event, we establish a quantifiable measurement of
the degree of realized expectation. The strength and distribution of output activations provide a method
for modeling:
1. Schema based theories of cognition.
2. Processes involved in resolving ambiguities and conflicts of schemas and patterns occurring at
different structural or hierarchical levels.
3. Dynamic contextualization. that is. how a context is created, adapted, and accepted or rejected as
it unfolds in time.
4. Expectational windows - how contexts create both short range and long range predictions. The
interaction of short term and long term memorv on these processes.
5. The influence of cyclic or metric organizers on pattern extraction and segmentation.
We propose to design and implement a series of experiments to investigate these implications and to
lerine and develop new connectionist architectures to build these models. Initial experiments with a
compact representation of a limited number of musical dimensions will be followed by a more flexible
representation incorporating all the multidimensionality. complexitv. and intricacies of a complete
musical work.
4-