A New Theory, Techniques and Alternatives for Direction-of-Arrival Estimation in Acoustic Signal Processing Bandhit Suksiri Fukumoto Research Laboratory, Kochi University of Technology August 22, 2019 Bandhit Suksiri (KUT) Public Defense August 22, 2019 1 / 49
Presentation Outline 1 Introduction: Preliminary Assessment, Objective, and New Approaches 2 Research #1: Acoustic DOA Estimation by using Theory of Orthogonal Procrustes Analysis 3 Research #2: Extension Theory of Orthogonal Procrustes Analysis for Acoustic DOA Estimation 4 Research #3: Acoustic DOA and Variance Estimation via Complex-Valued Tensor Factorization 5 Summary: Conclusions Bandhit Suksiri (KUT) Public Defense August 22, 2019 2 / 49
Presentation Outline 1 Introduction: Preliminary Assessment, Objective, and New Approaches 2 Research #1: Acoustic DOA Estimation by using Theory of Orthogonal Procrustes Analysis 3 Research #2: Extension Theory of Orthogonal Procrustes Analysis for Acoustic DOA Estimation 4 Research #3: Acoustic DOA and Variance Estimation via Complex-Valued Tensor Factorization 5 Summary: Conclusions Bandhit Suksiri (KUT) Public Defense August 22, 2019 3 / 49
Introduction: Direction-of-Arrival in the Field of Acoustics z φ 3 φ 2 φ 1 Source #3 Reference point Microphone arrays θ 3 θ 2 θ 1 Source #2 x Source #1 θ is a x -subarray DOA, φ is a z -subarray DOA. Usages of acoustic Direction-of-Arrival (DOA) estimation ◮ Speech enhancement, source separation, human computer interaction ◮ Other applications; surveillance, automatic camera management Bandhit Suksiri (KUT) Public Defense August 22, 2019 4 / 49
Introduction: Examples of the Applications Microphone Array Drone Target Location #1 Target Location #1 Target Location Target Location #2 Target Location #2 Reference: Reference: Satoshi Tadokoro. (2017, December 25) Impulsing Paradigm Change through Liu, Huawei; Li, Baoqing; Yuan, Xiaobing; Zhou, Qianwei; Huang, Disruptive Technologies Program (ImPACT): Tough Robotics Challenge. Jingchang. 2018. "A Robust Real Time Direction-of-Arrival Estimation Retrieved from http://www.jst.go.jp/impact/en/program/07.html. Method for Sequential Movement Events of Vehicles." Sensors 18, no. 4: 992. Bandhit Suksiri (KUT) Public Defense August 22, 2019 5 / 49
Introduction: Preliminary Assessment ◮ Discussion : The existing framework of acoustic DOA estimation Acoustic DOA Estimation DOA Estimation for Wireless Communication Level of Robustness Classic & Simplest Wideband Method Narrowband Method High Efficiency ◮ Problem : Lack of high efficient techniques and suitable theory when it comes to the acoustic DOA estimation gg ez Bandhit Suksiri (KUT) Public Defense August 22, 2019 6 / 49
Introduction: Preliminary Assessment ◮ Discussion : The existing framework of acoustic DOA estimation Acoustic DOA Estimation DOA Estimation for Wireless Communication Level of Robustness Classic & Simplest Wideband Method Narrowband Method Extension of framework, theory, techniques High Efficiency ◮ Solution : Propose a new theory to extend the existing framework form wireless communication field to acoustic signal processing Bandhit Suksiri (KUT) Public Defense August 22, 2019 7 / 49
Introduction: Research Objective and Contribution ◮ To propose an alternative theory for acoustic DOA estimation ; this theory enables some useful techniques in wireless communication field to implement an acoustic DOA estimation for reducing a computational complexity and facilitating the estimation algorithm. Bandhit Suksiri (KUT) Public Defense August 22, 2019 8 / 49
Introduction: Research Objective and Contribution ◮ To propose an alternative theory for acoustic DOA estimation ; this theory enables some useful techniques in wireless communication field to implement an acoustic DOA estimation for reducing a computational complexity and facilitating the estimation algorithm. ◮ Keywords: Bandhit Suksiri (KUT) Public Defense August 22, 2019 8 / 49
Introduction: Research Objective and Contribution ◮ To propose an alternative theory for acoustic DOA estimation ; this theory enables some useful techniques in wireless communication field to implement an acoustic DOA estimation for reducing a computational complexity and facilitating the estimation algorithm. ◮ Keywords: ◮ Theory of Orthogonal Procrustes Analysis and its Extension Bandhit Suksiri (KUT) Public Defense August 22, 2019 8 / 49
Introduction: Research Objective and Contribution ◮ To propose an alternative theory for acoustic DOA estimation ; this theory enables some useful techniques in wireless communication field to implement an acoustic DOA estimation for reducing a computational complexity and facilitating the estimation algorithm. ◮ Keywords: ◮ Theory of Orthogonal Procrustes Analysis and its Extension ◮ DOA via High-Order Generalized Singular Value Decomposition Bandhit Suksiri (KUT) Public Defense August 22, 2019 8 / 49
Introduction: Research Objective and Contribution ◮ To propose an alternative theory for acoustic DOA estimation ; this theory enables some useful techniques in wireless communication field to implement an acoustic DOA estimation for reducing a computational complexity and facilitating the estimation algorithm. ◮ Keywords: ◮ Theory of Orthogonal Procrustes Analysis and its Extension ◮ DOA via High-Order Generalized Singular Value Decomposition ◮ DOA via Complex-Valued Tensor Factorization Bandhit Suksiri (KUT) Public Defense August 22, 2019 8 / 49
Presentation Outline 1 Introduction: Preliminary Assessment, Objective, and New Approaches 2 Research #1: Acoustic DOA Estimation by using Theory of Orthogonal Procrustes Analysis 3 Research #2: Extension Theory of Orthogonal Procrustes Analysis for Acoustic DOA Estimation 4 Research #3: Acoustic DOA and Variance Estimation via Complex-Valued Tensor Factorization 5 Summary: Conclusions Bandhit Suksiri (KUT) Public Defense August 22, 2019 9 / 49
Research #1: System Overviews z Frequency STFT Amplitude φ 3 Time φ 2 Source #3 φ 1 Reference Point Frequency STFT Amplitude θ 3 Time Source #2 θ 2 Frequency θ 1 STFT Amplitude x Source #1 Time z x x ( t , f ) A x ( φ , f ) s ( t , f ) s ( t , f ) A z ( θ , f ) A x ( φ , f ) received signal at t,f x-subarray angle sources at t,f z ( t , f ) A z ( θ , f ) s ( t , f ) sources z-subarray angle x-subarray angle received signal at t,f x-subarray angle sources at t,f Unknown Parameters Observable Parameters Bandhit Suksiri (KUT) Public Defense August 22, 2019 10 / 49
Research #1: Microphone Structure Cramer-Rao bound (CRB) Z s k M Microphone Placement Structure Figure CRB Area 2 θ k 4,12, 16 ,20,... 1 Octagon Array 57 ϕ k (Circle Array) δ M 3 Y d 1 3,5,...,15, 17 ,19,... 60 2 L-Shaped Array X δ M 3 M 5,9,..., 17 ,21,.... 96 Cross Array δ M 3 3,6,9,12, 18 ,21,... 108 Triangle Array δ M 3 � 2 π d � 2 , CRB represents δ = 2SNR λ the lowest error bound of DOA. Bandhit Suksiri (KUT) Public Defense August 22, 2019 11 / 49
Research #1: Microphone Structure Cramer-Rao bound (CRB) Z s k M Microphone Placement Structure Figure CRB Area 2 θ k 4,12, 16 ,20,... 1 Octagon Array 57 ϕ k (Circle Array) δ M 3 Y d 1 3,5,...,15, 17 ,19,... 60 2 L-Shaped Array X δ M 3 M 5,9,..., 17 ,21,.... 96 Cross Array δ M 3 ◮ Octagon CRB has only 5% 3,6,9,12, 18 ,21,... 108 smaller than L-Shaped CRB. Triangle Array δ M 3 ◮ DOA for L-Shaped array is � 2 π d � 2 , CRB represents widely proposed recently. δ = 2SNR λ (more DOA technique) the lowest error bound of DOA. Y. Hua et al. , “L-shaped for estimating 2D DOA,” IEEE, 1991. Bandhit Suksiri (KUT) Public Defense August 22, 2019 11 / 49
General Model for Estimating DOA : source's variance each frequency Research #1: General Model via Cross-Correlation Signal Model at X-axis : Signal Model at Z-axis : A x ( φ , f ) s ( t , f ) A z ( θ , f ) s ( t , f ) x ( t , f ) z ( t , f ) signals received by the microphone array signals received by the microphone array Bandhit Suksiri (KUT) Public Defense August 22, 2019 12 / 49
Research #1: General Model via Cross-Correlation Signal Model at X-axis : Signal Model at Z-axis : A x ( φ , f ) s ( t , f ) A z ( θ , f ) s ( t , f ) x ( t , f ) z ( t , f ) signals received by the microphone array signals received by the microphone array General Model for Estimating DOA : source's variance each frequency x ( t , f ) z ( t , f ) H A x ( φ , f ) s ( t , f ) s ( t , f ) H H A z ( θ , f ) R S ff xz { , } ff { , } Bandhit Suksiri (KUT) Public Defense August 22, 2019 12 / 49
Research #1: Well-known Direction-of-Arrival Methods General Model for Estimating DOA : source's variance each frequency H A x ( φ , f ) H x ( t , f ) z ( t , f ) s ( t , f ) s ( t , f ) A z ( θ , f ) H R S ff xz { , } ff { , } Singular Value Decomposition & Estimating Left (and Right) Matrices Well-known methods for estimating subarray angles: ◮ Multiple Signal Classification (MUSIC) [R.Schmidt, 1986] ◮ Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT) [R.Roy et al. , 1989] ◮ 2-dimensional MUSIC [M.G.Porozantzidou et al. , 2010] Bandhit Suksiri (KUT) Public Defense August 22, 2019 13 / 49
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