November 22, 2004 —
A University of Southern California biomedical engineer's pioneering brain cell
research has led directly to a patented system that is now being rolled out to
stem gun violence on the streets of Chicago and, soon, Los Angeles.
A recently-developed microphone surveillance system uses his insights to recognize
— instantly, and with high accuracy — the sound of a gunshot, and only a gunshot,
within a two-block radius. It can then locate, precisely, where the shot was
fired; turn a camera — if one is attached — to center the shooter in the camera
viewfinder, and make a 911 call to a central police station. The police can then
take control of the camera to track the shooter and dispatch officers to the scene.
The city of Chicago is now installing the first five of a planned 80 of the
devices in high crime neighborhoods, supplementing existing cameras. In Los Angeles
County, Sheriff Lee Baca is now soliciting community involvement and participation
to deploy 10 of the units in a pilot test, to be followed by more if the results
are successful.
Algorithms devised by Berger, who holds the David Packard Chair in the US Viterbi
School of Engineering's department of biomedical engineering, are at the heart
of the "SENTRI" system built by an Oak Brook, Illinois-based firm named Safety Dynamics, a company in which Berger holds the position of Chief Scientist.
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SENTRI system as installed in Chicago |
SENTRI uses acoustic recognizers, posted in trios or larger groupings on utility
poles or other listening posts, which are tuned to certain specific warning sounds
with extremely high accuracy. "A simple loud noise, even an explosive noise, won't
set them off," Berger says.
The device is listening for the entire sound pattern of the gunshot, not just
the initial explosion, which makes it much less likely to mistake other loud noises
for shooting.
A specially configured computer system (a "directional analyzer") accurately
calculates any authenticated gunshot's location - using the difference in the
time the sound arrives at the different microphones on a SENTRI acoustic units
unit. It then points a camera, turns on lights, sounds an alarm, and alerts police
units.
Field tests with handguns have shown 95% accuracy with respect to gunshot recognition,
and 100% accuracy with respect to centering the camera on the shooter for those
recognized gunshots.
SENTRI is an acronym for "Smart Sensor Enabled Neural Threat Recognition and
Identification." The "neural" in the title refers directly to Berger's work, which
was based on analysis of the "language" nerve cells, or neurons, use to convey
information, and specifically on his modeling of the way the brain forms memories
of sounds
Neurons only way of distinguishing signals is to fire repeatedly, either faster
or slower, in different temporal patterns. "It is the time difference between
pulses that carries the information," Berger says. "This is a coding completely
unlike that used by computers, which are collections of ones and zeros, changing
to the beat of a constant clock."
Working with computer specialists, however, Berger has created neural-like computer
systems that can model the neural time coding and make distinctions the way nerves
do.
Four years ago, he
and a colleague used the technique to demonstrate the first speech recognition
system that could pick words out of ambient noise as well as humans can.
While work continues on speech recognition applications, the systems need training
to learn individual signals. For language, this is very time consuming because
the system has to learn each individual word.
"But for alarm signals," says Berger, "you start with a relatively small number
of sounds you have to distinguish with high accuracy — gunshots, for example,
or diesel engines for border patrol crossings or oil pipeline thieves, or chainsaws
(and diesels) to listen for outlaw loggers. This vocabulary is quite manageable."
Machine sounds are the only ones in SENTRI's vocabulary. It cannot eavesdrop
on conversations, the scientist emphasizes.
