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.