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Carnegie Mellon scientists show brain uses optimal code for sound

des the whole range of nature sounds, including components of speech such as vowels and consonants, and natural environment sounds like footsteps in a forest or a flowing stream," said Michael Lewicki, associate professor of computer science at Carnegie Mellon and a member of the Center for the Neural Basis of Cognition (CNBC). "We found that the optimal code for natural sounds is the same as that for speech. Oddly enough, cats share our own optimal auditory code for the English language."

"Our work is the only research to date that efficiently processes auditory code as kernalized spikes," said Evan Smith, a graduate student in psychology at the CNBC.

Until now, scientists and engineers have relied on Fourier transformations--initially discovered 200 years ago--to separate and re-constitute parameters like frequency and intensity as part of traditional sound signal processing.

"Our new signal processing framework appears far more efficient, effective and concise in conveying a rich variety of natural sounds than anything else," Lewicki said.

The approach by Smith and Lewicki dissects sound based only on the timing of compressed "spikes" associated with vowels (like cat vocalizations), consonants (like rocks hitting one another) and sibilants (ambient noise).

To gather sounds for their research, the scientists traipsed through the woods and recorded cracking branches, crunching leaves and wind rustling through leaves before returning to the laboratory to de-code the information contained in this rich set of sounds. They also discovered what they consider the most "natural" sound: if they play back a random set of spikes, it sounds like running water.

"We're very excited about this work because we can give a simple theoretical account of the auditory code which predicts how we could optimize signal processing to one day allow for much more efficient data storage on everything from DVDs to iPods," Lewicki said.

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Source:Carnegie Mellon University


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