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Explore various methods such as Least Square Method, High Order Ambisonics, and Wave Field Synthesis for sound field reconstruction. Learn about Propagation Matrix, SVD, Spherical Harmonics, and more.
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THEORETICAL STUDY OFSOUND FIELD RECONSTRUCTIONF.M. Fazi P.A. Nelson
Different Techniques • Least Square Method (LSM) • Based on minimising the error between the target and reconstructed sound field • High Order Ambisonics (HOA) • Based on the Fourier-Bessel analysis of the sound filed • Wave Field Synthesis (WFS) • Based on the Kirchhoff-Helmholtz integral
LSM: basic principle • Loudspeaker complex gains (vector a) are obtained by directly filtering the microphone signals (vector p) • This process can be represented as p=Ca p a C
LSM: basic principle • Vector p represents the microphone signals obtained measuring the original sound field. • p represents the microphone signals obtained by measuring the reconstructed sound field. • The target is to chose the loudspeaker gains that minimise p p
LSM: Propagation Matrix • It is possible to compute or measure the propagation matrix H. • Element Hk,l represents the transfer function between the l-th loudspeaker and the k-th microphone • The mean square error is now Matrix H
LSM: solution and SVD • The solution to the inverse problem (matrix C) is given by the pseudo-inversion of the propagation matrix • Applying the Singular Value Decomposition, the propagation matrix can be decomposed as Σ is a non negative diagonal matrix containing the singular values of H U, V are unitary matrices, which represent orthogonal bases • The computation of Matrix C becomes:
Linear algebra and functional analysis p(x) Yi(x) v êi x
SVD – Linear algebra x2 v M y2 êi w x1 y1 ĝi
SVD – Functional analysis Sx x Sy y
SVD - Encoding and decoding • SVD allows the separation of the encoding and decoding process • The regularisation parameter β allows the design of stable filters p a C p a UH V DECODING ENCODING
LSM: concentric spheres r1 r2 Spherical Harmonics
LSM: concentric spheres r1 r2 Spherical Harmonics Hankel and Bessel Functions
LSM: concentric spheres r1 r2
Important Consequences • It is possible to analytically compute the singular values of matrix H. • They depend on the transducers radial coordinates only. • The conditioning of matrix Hstrongly depends on the microphones radial coordinate. • Thesingular functions of matrix H and represent the spherical harmonics.
Normalized Mean Square Error Microphone radial position Zero order Bessel function
Limited number of transducers • The presented results hold for a continuousdistribution of loudspeakers and microphones (infinite number of transducers). • Problems related to the use of a limited number of transducers: • Matrices U and V represent not complete bases • Spatial aliasing (affects all methods) • Regular sampling problem • Matrices U and V are not orthogonal if defined analytically (but are orthogonal using LSM)
Comparison of reconstruction methods • If the number of transducers is infinite LSM, HOA and KHE are equivalent in the interior domain . • The KHE only allows controlling the sound field in the exterior domain, but requires both monopole and dipole like transducers • If the number of transducers is finite, different methods are affected by different reconstruction errors.
Least Squares Method Original sound filed Kirchhoff Helmholtz Equation High Order Ambisonics
Conclusions • The basics of Least Squares Method have been presented. • The meaning of the generalised Fourier transform and Singular Value Decomposition has been illustrated. • It has been shown that HOA and the simple source formulation could be interpreted as special cases of the LSM Further research • To design a device for themeasurementand analysis of a real sound field. • To design a system for analysing the sound filed generated by realacoustic sources. • To design a system for the reconstruction and synthesisof 3D sound fields using an (almost) arbitrary arrangement of loudspeaker.
Original Sound Field LSM with regularisation LSM eccentric spheres 1 LSM eccentric spheres 2