Personal audio systems generate a local sound field for a listener while attenuating the sound energy in predefined quiet zones. In practice, system performance is sensitive to errors in the acoustic transfer functions between the sources and the zones. In this paper, a design framework for robust reproduction is proposed that combines transfer functions and error modeling. The framework allows a physical perspective on the regularization required for a system that is based on there being a bound on the assumed additive or multiplicative errors, which is obtained by acoustic modeling. Acoustic contrast control is separately combined with worst-case and probability-model optimization, exploiting limited knowledge of the potential error distribution. Monte-Carlo simulations show that these approaches give increased system robustness compared to the state-of-the-art approaches for regularization parameter estimation. Experimental results verify that robust sound zone control can be achieved in the presence of loudspeaker gain errors. In addition, to simplify the approach, in-situ transfer function measurements were reduced to a single measurement per loudspeaker per zone with limited acoustic contrast degradation of less than 2 dB over 100–3000 Hz compared to the fully measured regularized case.
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