Tim Habigt. May 30, 2014
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1 Slide 1/26 Sound-Source Localization Tim Habigt May 2014 Sound-Source Localization Tim Habigt Lehrstuhl für Datenverarbeitung May 30, 2014
2 Slide 2/26 Sound-Source Localization Tim Habigt May 2014 Table of contents Sound source localization Microphone arrays / binaural robots Time delay estimation Beamforming Binaural localization
3 Slide 3/26 Sound-Source Localization Tim Habigt May 2014 Microphone arrays? (a) Array? Figure: Acoustic locators (around 1920) (b) Vertical localization
4 Slide 4/26 Sound-Source Localization Tim Habigt May 2014 Microphone arrays (a) Microsoft Kinect Figure: Commercial arrays (b) Playstation Eye
5 Microphone arrays (a) CCRL array (b) MIT array with 1020 microphones Figure: Larger arrays Slide 5/26 Sound-Source Localization Tim Habigt May 2014
6 Slide 6/26 Sound-Source Localization Tim Habigt May 2014 Binaural localization Figure: Binaural localization
7 Videos Slide 7/26 Sound-Source Localization Tim Habigt May 2014
8 Slide 8/26 Sound-Source Localization Tim Habigt May 2014 Signal propagation Figure: Coordinate systems 1 1 courtesy of Marko Durkovic
9 Slide 9/26 Sound-Source Localization Tim Habigt May 2014 Signal propagation Figure: Time delay 2 2 courtesy of Marko Durkovic
10 Slide 10/26 Sound-Source Localization Tim Habigt May 2014 Signal propagation x 0 (t) = x 1 (t) = s(t) s(t t) t = d c sin(α) Speed of sound c = 343 m s
11 Slide 10/26 Sound-Source Localization Tim Habigt May 2014 Signal propagation x 0 (t) = x 1 (t) = s(t) s(t t) t = d c sin(α) Speed of sound c = 343 m s
12 Slide 10/26 Sound-Source Localization Tim Habigt May 2014 Signal propagation x 0 (t) = x 1 (t) = s(t) s(t t) t = d c sin(α) Speed of sound c = 343 m s
13 Slide 11/26 Sound-Source Localization Tim Habigt May 2014 Localization techniques Time difference of arrival (TDOA) Steered response power (beamforming) HRTF-based binaural localization
14 Slide 12/26 Sound-Source Localization Tim Habigt May 2014 TDOA-based localization Cross-correlation Auto-correlation R xy [m] = R xx [m] = n= n= m denotes the shift denotes complex conjugation x[n]y [n + m] (1) x[n]x [n + m] (2)
15 Slide 13/26 Sound-Source Localization Tim Habigt May 2014 TDOA-based localization Cross-correlation demo
16 Slide 14/26 Sound-Source Localization Tim Habigt May 2014 TDOA-based localization Figure: Time delay 3 Drawbacks: Binaural localization via time delays is ambiguous ( cone of confusion ) 3 courtesy of Marko Durkovic
17 Slide 15/26 Sound-Source Localization Tim Habigt May 2014 Beamforming Figure: Beam pattern 4 4 courtesy of Dr. Andrew Greensted
18 Slide 16/26 Sound-Source Localization Tim Habigt May 2014 Beamforming Figure: Signal summation 5 5 courtesy of Dr. Andrew Greensted
19 Slide 17/26 Sound-Source Localization Tim Habigt May 2014 Beamforming Signal summation demo
20 Slide 18/26 Sound-Source Localization Tim Habigt May 2014 Beamforming Figure: Beamforming 6 6 courtesy of Dr. Andrew Greensted
21 Slide 19/26 Sound-Source Localization Tim Habigt May 2014 Time shift x(t t) = x(ω)e jω t j2πf t = x(ω)e Frequency domain processing demo
22 Slide 20/26 Sound-Source Localization Tim Habigt May 2014 Multiple microphones x 0 (t) = x 1 (t) = x 2 (t) = s(t) s(t t) s(t 2 t) x 0 (ω) = 1 s(ω) x 1 (ω) = e jω t s(ω) x 2 (ω) = e jω2 t s(ω) 1 x(ω) = e jω t s(ω) e jω2 t x(ω) = a(α d )s(ω) a is called steering vector
23 Slide 20/26 Sound-Source Localization Tim Habigt May 2014 Multiple microphones x 0 (t) = x 1 (t) = x 2 (t) = s(t) s(t t) s(t 2 t) x 0 (ω) = 1 s(ω) x 1 (ω) = e jω t s(ω) x 2 (ω) = e jω2 t s(ω) 1 x(ω) = e jω t s(ω) e jω2 t x(ω) = a(α d )s(ω) a is called steering vector
24 Slide 20/26 Sound-Source Localization Tim Habigt May 2014 Multiple microphones x 0 (t) = x 1 (t) = x 2 (t) = s(t) s(t t) s(t 2 t) x 0 (ω) = 1 s(ω) x 1 (ω) = e jω t s(ω) x 2 (ω) = e jω2 t s(ω) 1 x(ω) = e jω t s(ω) e jω2 t x(ω) = a(α d )s(ω) a is called steering vector
25 Slide 20/26 Sound-Source Localization Tim Habigt May 2014 Multiple microphones x 0 (t) = x 1 (t) = x 2 (t) = s(t) s(t t) s(t 2 t) x 0 (ω) = 1 s(ω) x 1 (ω) = e jω t s(ω) x 2 (ω) = e jω2 t s(ω) 1 x(ω) = e jω t s(ω) e jω2 t x(ω) = a(α d )s(ω) a is called steering vector
26 Slide 21/26 Sound-Source Localization Tim Habigt May 2014 Beamforming Figure: Beamformer
27 Slide 22/26 Sound-Source Localization Tim Habigt May 2014 Delay and Sum Beamforming y[n] = w H x[n] To align all the microphone signals choose w = a(θ d )
28 Slide 23/26 Sound-Source Localization Tim Habigt May 2014 Delay and Sum Beamforming Delay and Sum Beamforming demo
29 Slide 24/26 Sound-Source Localization Tim Habigt May 2014 Delay and Sum Beamforming Figure: Spatial aliasing No aliasing if d c f
30 Binaural localization Head-Related Transfer Function (HRTF) HRTF describes influence of head and pinna on the incoming sound wave Spatial dependency Slide 25/26 Sound-Source Localization Tim Habigt May 2014
31 Slide 26/26 Sound-Source Localization Tim Habigt May 2014 Cross-convolution localization Convolve microphone signal with all pairs of HRTFs (left and right ear switched) Correct location η maximizes similarity
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