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An auditory system modeling in sound localization

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

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An auditory system modeling in sound localization

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

  2. 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]

  3. example block diagram[Lim&Duda,1994]

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

  5. 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

  6. Where I am • computation issues: data representation (int/real, bipolar/unipolar), dataset size (resolution/time) 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

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