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Mark Hasegawa-Johnson January 29, 2003

Audiovisual Display and Audiovisual Recognition in Free Field Environments: Caves, Cars, and Critical Bands. Mark Hasegawa-Johnson January 29, 2003 Collaborators: Bowon Lee, Camille Goudeseune, Zhinian Jing, Danfeng Li, Thomas Huang, Stephen Levinson. What is “free-field audio?”.

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Mark Hasegawa-Johnson January 29, 2003

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  1. Audiovisual Display and Audiovisual Recognition in Free FieldEnvironments: Caves, Cars, and Critical Bands Mark Hasegawa-Johnson January 29, 2003 Collaborators: Bowon Lee, Camille Goudeseune, Zhinian Jing, Danfeng Li, Thomas Huang, Stephen Levinson

  2. What is “free-field audio?”

  3. What is “free-field audio?”

  4. Problems of free-field audio • CONTROL: acoustics of the room • CONTROL: where the user stands/sits • CONTROL: background noise

  5. Topic #1: Audio Display for a Six-Walled Virtual Reality Theater (Beckman CUBE)

  6. Audio Demo: the Plywood CUBE

  7. What does the User Hear?

  8. Measuring the Impulse Response of the Room: 2X2 Locations

  9. Measurement of the Impulse Response: Starter Pistol SNR

  10. Measurements: Gun-Related Transfer Function

  11. What do we WANT the User to Hear?

  12. Solution: Regularized Semi-Inversion of Matrix Frequency Response

  13. Image Source Method for Simulating the Room Response

  14. Comparison of Measured and Simulated Room Responses

  15. One More Problem: Image Source Method only accurate for t<100ms

  16. Heuristic solution: Window the simulation with a decaying window

  17. Results: 12dB Dereverberation, 15dB Early Echo Suppression

  18. Topic #2: Audiovisual Speech Recognition in a Moving Car

  19. Solution: Two Cameras, Two Microphones

  20. Lip Motion Tracking(Tao and Huang, 1999)

  21. Audiovisual Speech Recognition: Audio and Visual Integration(Chu and Huang, 2002)

  22. AVSR Results

  23. Two-Microphone Beamforming using an LSE Beamformer

  24. Recognition Features based on Auditory Scene Analysis1: Bandpass Filters on a Semilog Freq Scale (Simulate the Inner Ear)

  25. Recognition Features based on Auditory Scene Analysis: 2. Correlogram

  26. Recognition Features based on Auditory Scene Analysis: Correlogram Band Center Frequency Sub-band Pitch Period

  27. Recognition Features based on Auditory Scene Analysis: 3. Periodicity-Weighted Spectrum(Jing and Hasegawa-Johnson, 2001)

  28. Digit Recognition Results

  29. Conclusions • Image source simulated room response • 12dB dereverberation, 15dB early echo suppression • Speech recognition in a car: Two Cameras, Two Microphones. • Speech Rec w/ Auditory Scene Analysis: error rate halved at 0dB.

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