Sound Localization PART 2 Ali Javed, Josh Manuel, Brunet Breaux, Michael Browning.
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Transcript of Sound Localization PART 2 Ali Javed, Josh Manuel, Brunet Breaux, Michael Browning.
Sound Localization PART 2
Ali Javed, Josh Manuel, Brunet Breaux, Michael Browning
Milestone 1 to do list:
• Build Sample Space• Make Sample Point Database• Interface Video Recording Camera code• Adjust RLS filter• Optimize/Test Program
Sample Space
• Test Area• 10’ x 10’ x 8’
• Interior grid• 2’ x 2’ x 2’
• Sound source stand• 8’ Height, 2’ Increments • Mobile
• Microphones• 4 at different Corners
Sample Point Database
• We were able to build it.• The data we collected wasn’t consistent
enough for us to localize sound.
Problems with our original sample point database
• Sound source used• Room noise threshold • Consistency of hardware
measurements
Methods for Improving Onset Results
• Calibrate room noise level for each mic individually
• Find more reliable sound source to create database
• Test different room noise threshold multipliers
• Multilateration
Video Recording
• Interface Video Recording Camera code– Initializing camera
• Adaptor, Device id, Format and Resolution
– Set Recording length• ‘Bufferlength’ Variable
– AviObject• Name, Compression, FPS, and Quality
ImFrame Loop
– Camera Trigger• vid.framesPerTrigger=X
– aviObject = close(aviOblect);
RLS Filter
• Last time– Not filtering properly– Too slow
• Now– Filtering well
• Improves sound to noise ratio by about 4 times• Was doing this before
– Plotting method was erroneous, not filter
– Still slow• Even with minimal sample comparison length
Euclidean Distance Code
Euclidean Distance Code
MultilaterationTime Difference of Arrival (TDOA)
• Simple trigonometric difference comparison calculation
• By solving a system of three equations each using a different mic comparison for d, e.g. absolute value of onset A – onset B, X Y and Z coordinates can be calculated
MultilaterationTime Difference of Arrival (TDOA)
• Pros– Does not rely on database– Margin of error can be calculated– Effective closer to center of the test space
• Cons– Ineffective on edges of test space– Must pick ‘base’ mic used in each comparison
• Possible fixes– Move mics further from corners– Average values of four different system solutions
• One for each ‘base’ mic option
Future Goals:
• Finalize Video code• Implement GUI• Optimize signal collection for accurate
localization• Determine minimum sound source
decibel level and accuracy of final product
• Make it work and look good doing it