People Centric Processing
Better CHI with Signal Computation

Malcolm Slaney
Interval Research Corporation

Seminar on People, Computers, and Design
Stanford University January 7, 2000

One of the most difficult problems in computer science is recognizing and synthesizing the full range of human behavior. It is important to allow humans to deal with computers on human terms. I will describe several techniques that my colleagues and I have developed that use signal computation to improve human-computer interaction. I will talk about algorithms that sense human behavior (BabyEars), entertain our senses (audio morphing and Magic Mirror), synthesize realistic humans (Video Rewrite) and improve human perception (Mach1 and auditory scene analysis). We call this style of computation people-centric processing.

Malcolm Slaney is a signal processor at Interval Research, where he looks for new ways to use lots of CPU cycles. He received his PhD in Electrical Engineering from Purdue University for his work on diffraction tomography algorithms. Since then he has worked on modeling the auditory system, automatic speech recognition, architectures for image processing, computational environments for signal processing, and lots of wonderful ways to create, modify, characterize, abuse and display audio and video signals. He also organizes the Stanford CCRMA Hearing Seminar..


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