By K.C. Jones
TechWeb Technology News
August 24, 2006
Researchers at the University of Missouri-Columbia are hoping computer
programmers can help them with a solution to a decades-old "cocktail
The researchers have found a mathematical solution that allows them to
separate one sound from a recording of a noisy environment -- like a
single voice from the din of conversation at a cocktail party.
Mathematics professors Dan Casazza and Dan Edidin and Radu Balan, of
Siemens Corporate Research, solved the problem and demonstrated that
it is possible to isolate distinct voices and reconstruct spoken
"Our solution is called 'signal reconstruction without noisy phase,'"
Edidin said. "In speech recognition technology, a 'signal' could be a
recording of 25 people in a room talking at the same time. Our
solution shows that we can pull out each voice individually, not just
with the words, but with the voice characteristics of each individual.
We showed that this 'cocktail party problem' is mathematically
The National Science Foundation and the National Security Agency
funded part of the research, which could have crime fighting, homeland
security and other intelligence applications.
In the past, researchers were able to separate voices but not
reproduce the characteristics of the voice itself, according to a
statement from the university. Casazza said existing programs that can
separate and reconstruct voices are not completely reliable because
they have difficulty separating voices with similar pitch
characteristics. The researchers claim that a program using their
solution would be more exact.
"Theoretically, our solution says you should be able to pick up voices
on a squeaky old microphone and then separate them all out so that you
can hear what each person is saying in his or her own voice," Casazza
said. "This is a very old problem, and we have the first mathematical
solution to it."
However, they were not able to create an algorithm that will produce
"The computer we use is doing the work without an algorithmic program.
It uses a system called a neural net, which is designed for the
computer to teach itself. Basically, it works on trial and error,"
Casazza said. "This isn't consistent and cannot be duplicated easily.
We need to find a way to design an implementable algorithm that could
do this consistently and quickly."
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