Archive

In this paper, we present soft computing tools and techniques aimed at realizing musical instruments that learn. Specifically we explore applications of neural network and fuzzy logic techniques to the design of instruments that form highly personalized relationships with their users through self-adaptation. We demonstrate techniques for adapting sensor arrays and techniques for realizing highly expressive real-time sound synthesis algorithms.

Database of Challenging Musical Sounds for
Evaluation and Refinement of Pitch Estimators

Adrian
Freed, Tristan Jehan

Bring Your Own Control to
Additive Synthesis

Adrian Freed

The Rebirth of Computer Music by Analog Signal
Processing

Adrian Freed

ABSTRACT

Bi-directional AES/EBU Digital Audio and Remote Power over
a single Cable

Adrian Freed

CNMAT, UC Berkeley, 1750 Arch Street, Berkeley, CA 947709

510 643 9990 adrian@cnmat.berkeley.edu

Musical Applications of New Filter Extensions to
Max/MSP

Tristan Jehan, Adrian Freed, Richard Dudas

CNMAT, UC Berkeley, 1750 Arch Street, Berkeley, CA 94709

(510) 643 9990 {tristan,adrian,dudas}@cnmat.berkeley.edu

Second-order Recursive Oscillators for Musical Additive
Synthesis Applications on SIMD and VLIW Processors

Todd Hodes, Adrian Freed

Computer Science Division
University of California, Berkeley
hodes@cs.berkeley.edu

Center for New Music and Audio
Technologies
University of California, Berkeley
adrian@cnmat.berkeley.edu

Communication of Musical Gesture using the AES/EBU
Digital Audio Standard

Adrian Freed and David Wessel

CNMAT, 1750 Arch Street, Berkeley, CA 94709

(510) 643 9990, {adrian,wessel}@cnmat.berkeley.edu

Music Programming with the new Features of
Standard C++

Adrian Freed and Amar Chaudhary
CNMAT, 1750 Arch Street. Berkeley, CA 94709
(510) 643 9990, adrian@cnmat.berkeley.edu