An auditory system modeling in sound localization

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Presentation transcript:

An auditory system modeling in sound localization EE381K MDDSP May. 5 2005 Yul Young Park

Previous Talk Sound Localization motivations: virtual 3D sound, game, sonar, microphone array, objective sound quality evaluation, education, etc Goal: estimate correct azimuth and elevation (distance -> ignored) Conventional approach: neural network [Neti&Young,1992] vs probabilistic estimator [Lim&Duda,1994, Chau&Dudda,1995, Martn,1995] Suggested method: improve neural network model by evolutionary computation [Stanley,2002]

example block diagram [Lim&Duda,1994]

Neural Network training data, test data - network output: - desired output: - mean square error of network: gradient descent method: decrease the error by adjusting training data, test data issues: local minimum, generalization, data representation

Evolutionary Computation [stanley,2002] Local Minimum, partly Generalization Evolution on Node and connection genes: population -> evaluation (fitness) -> reproduce (ranking/sampling/crossover or mutation/speciation) Innovation record: historical marking -> easy topology analysis

Cochlear & Neural Signal Model Where I am HRTF Cochlear & Neural Signal Model Pre-Processing Neuroevolution Elevation: -40°~90°, 10° increment Azimuth: 5°~30° at each elevation Total 710 points max. gammatone filterbank &half wave rectification 64 ch. along 500Hz ~22.05kHz ITD: cross-correlation IID: subtraction population size fitness evaluation computation issues: data representation (int/real, bipolar/unipolar), dataset size (resolution/time)