Bayesian Sound Field Estimation Using Uncertain Data

Accurate sound field estimates are often cumbersome to obtain, since they generally rely on microphone measurements at several spatial positions within a room. In addition, room acoustics are often non-stationary, in which case the sound field has to be repeatedly re-estimated with new measurements. However, since the new and old measurements are recorded in different acoustic environments, it is not straightforward to fully exploit the combined measurements. In this paper, a Bayesian approach is taken where older measured data are considered to be more uncertain than newer data. The proposed method allows for the use of data captured in different acoustic environments. For each set of measurements, the position, directivity, and number of microphones are allowed to differ. It is demonstrated on real sound field measurements that the proposed approach is effective, being able to better account for different levels of uncertainty in the data.