Sound Field Decomposition with Spherical Microphone Arrays Using Sparse Recovery Techniques

Abstract

This dissertation describes research regarding sound field decomposition with spherical microphone arrays using sparse recovery techniques. Recently, sound field decomposition using sparse recovery has demonstrated the ability to achieve a surprisingly high-resolution spatial analysis of the sound field. The focus of this thesis is on improving the accuracy of the sound field decomposition in non-sparse conditions with multiple sources and reverberation. In particular, we develop and characterise two new sparse recovery techniques, which improve the spatial accuracy of the sound field decomposition in non-sparse sound conditions. The first method incorporates information related to the expected location of the sound sources into the sparse recovery problem. The second method takes advantage of both independence and sparsity by serially combining the methods of independent component analysis and sparse recovery for sound field decomposition. We then go on to examine the issue of resolving sources based on their distance from the spherical microphone array. We develop a new sparse recovery method to resolve sources located in the same direction, but located at different distances. We then very briefly examine the decomposition of sound fields based on signal content. In other words, instead of trying to decompose the sound field based on spatial location, we seek to decompose the sound field based on phoneme or word content.