Structured sparse signal models and decomposition algorithm for super-resolution in sound field recording and reproduction

A method for achieving super-resolution of sound field recording and reproduction is proposed. To obtain driving signals of loudspeakers for reproduction from received signals of microphones, sparse signal decomposition makes it possible to reduce spatial aliasing artifacts when the number of microphones is less than that of loudspeakers. For more accurate and robust signal decomposition, we propose three types of group sparse signal model based on the physical properties of a sound field. In addition, a decomposition algorithm is derived to address these signal models as an extension of M-FOCUSS. In the simulation experiments, the accuracy of the sparse decomposition was significantly improved compared with that of M-FOCUSS. Furthermore, the accuracy of sound field reproduction using our proposed method was higher than that using current methods, especially at frequencies above the spatial Nyquist frequency.

[1]  Shoichi Koyama,et al.  Sparse sound field representation in recording and reproduction for reducing spatial aliasing artifacts , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[2]  Bhaskar D. Rao,et al.  Sparse solutions to linear inverse problems with multiple measurement vectors , 2005, IEEE Transactions on Signal Processing.

[3]  Jonathan Le Roux,et al.  Source localization in reverberant environments using sparse optimization , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  A. Berkhout,et al.  Acoustic control by wave field synthesis , 1993 .

[5]  Dmitry M. Malioutov,et al.  A sparse signal reconstruction perspective for source localization with sensor arrays , 2005, IEEE Transactions on Signal Processing.

[6]  Sascha Spors,et al.  Multichannel Adaptive Filtering with Sparseness Constraints , 2012, IWAENC.

[7]  Sascha Spors,et al.  An analytical approach to local sound field synthesis using linear arrays of loudspeakers , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Joel A. Tropp,et al.  Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit , 2006, Signal Process..

[9]  Shoichi Koyama,et al.  Map estimation of driving signals of loudspeakers for sound field reproduction from pressure measurements , 2013, 2013 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics.

[10]  Volkan Cevher,et al.  Model-based sparse component analysis for reverberant speech localization , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[11]  E. Williams,et al.  Fourier Acoustics: Sound Radiation and Nearfield Acoustical Holography , 1999 .

[12]  Craig T. Jin,et al.  Upscaling Ambisonic sound scenes using compressed sensing techniques , 2011, 2011 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA).

[13]  Alain Rakotomamonjy,et al.  Surveying and comparing simultaneous sparse approximation (or group-lasso) algorithms , 2011, Signal Process..

[14]  R. Rabenstein,et al.  The Theory of Wave Field Synthesis Revisited , 2008 .

[15]  Shoichi Koyama,et al.  Analytical Approach to Wave Field Reconstruction Filtering in Spatio-Temporal Frequency Domain , 2013, IEEE Transactions on Audio, Speech, and Language Processing.

[16]  Jont B. Allen,et al.  Image method for efficiently simulating small‐room acoustics , 1976 .

[17]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[18]  Bhaskar D. Rao,et al.  An Empirical Bayesian Strategy for Solving the Simultaneous Sparse Approximation Problem , 2007, IEEE Transactions on Signal Processing.