Abstract
Recent attention to the problem of controlling multiple loudspeakers to create sound zones has been directed toward practical issues arising from system robustness concerns. In this study, the effects of regularization are analyzed for three representative sound zoning methods. Regularization governs the control effort required to drive the loudspeaker array, via a constraint in each optimization cost function. Simulations show that regularization has a significant effect on the sound zone performance, both under ideal anechoic conditions and when systematic errors are introduced between calculation of the source weights and their application to the system. Results are obtained for speed of sound variations and loudspeaker positioning errors with respect to the source weights calculated. Judicious selection of the regularization parameter is shown to be a primary concern for sound zone system designers—the acoustic contrast can be increased by up to 50 dB with proper regularization in the presence of errors. A frequency-dependent minimum regularization parameter is determined based on the conditioning of the matrix inverse. The regularization parameter can be further increased to improve performance depending on the control effort constraints, expected magnitude of errors, and desired sound field properties of the system.
Highlights
Array signal processing techniques for sound zoning are derived from two approaches: sound field synthesis, where the entire sound field controlled by the array can be specified, and beamforming [e.g. 3]
The sound field synthesis techniques are characterized by accurate reproduction of the target sound field, this can be at the cost of excessive control effort [2] and zone contrast performance [1]
Whilst the parameter cannot be varied for Brightness control (BC), the scores are plotted at the point where they intersect with the value of λ (Eq (4)) required to achieve the target sound pressure in the bright zone
Summary
Array signal processing techniques for sound zoning are derived from two approaches: sound field synthesis (which can be solved analytically [e.g. 1] or by directly optimizing the complex pressures in the zones [e.g. 2]), where the entire sound field controlled by the array can be specified, and beamforming [e.g. 3]. The optimization cost function is written to directly minimize the error e between the desired sound field (in this case a plane wave in the target zone and zero pressure amplitude in the dark zone) and reproduced sound field, with an effort constraint for Tikhonov regularization: J = eH e + λ(qH q − E).
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
