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Sound Localization PART 2 Ali Javed, Josh Manuel, Brunet Breaux, Michael Browning.

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Presentation on theme: "Sound Localization PART 2 Ali Javed, Josh Manuel, Brunet Breaux, Michael Browning."— Presentation transcript:

1 Sound Localization PART 2 Ali Javed, Josh Manuel, Brunet Breaux, Michael Browning

2 Milestone 1 to do list: Build Sample Space Make Sample Point Database Interface Video Recording Camera code Adjust RLS filter Optimize/Test Program

3 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

4 Sample Point Database We were able to build it. The data we collected wasn’t consistent enough for us to localize sound.

5 Problems with our original sample point database Sound source used Room noise threshold Consistency of hardware measurements

6 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

7 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

8 ImFrame Loop –Camera Trigger vid.framesPerTrigger=X –aviObject = close(aviOblect);

9 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

10 Euclidean Distance Code

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12 Multilateration Time 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

13 Multilateration Time 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

14 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


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