A biologically motivated neural network for phase extraction from complex sounds.
We demonstrate that natural acoustic signals like speech or music contain synchronous phase information across multiple frequency bands and show how to extract this information using a spiking neural network. This network model is motivated by common neurophysiological findings in the auditory brainstem and midbrain of several species. A computer simulation of the model was tested by applying spoken vowels and organ pipe tones. As expected, spikes occurred synchronously in the activated frequency bands. This phase information may be used for sound separation with one microphone or sound localization with two microphones.
University of Ulm, Department of Neuroinformatics, 89069 Ulm, Germany. Marcus.Borst@informatik.uni-ulm.de
Marcus
M
Borst
Gerald
G
Langner
Günther
G
Palm
D000161:Acoustic Stimulation / Q000379:methods; D000200:Action Potentials / Q000502:physiology; D000818:Animals; D001306:Auditory Pathways / Q000502:physiology*; D003198:Computer Simulation; D006801:Humans; D008959:Models, Neurological; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D010470:Perceptual Masking / Q000502:physiology; D010507:Periodicity; D013016:Sound; D013017:Sound Localization; D013067:Speech Perception / Q000502:physiology; D013997:Time Factors