Parametric modeling of room acoustics aims at representing room transfer functions by means of digital filters and finds application in many acoustic signal enhancement algorithms. In previous work by other authors, the use of orthonormal basis functions (OBFs) for modeling room acoustics has been proposed. Some advantages of OBF models over all-zero and pole-zero models have been illustrated, mainly focusing on the fact that OBF models typically require less model parameters to provide the same model accuracy. In this paper, it is shown that the orthogonality of the OBF model brings several additional advantages, which can be exploited if a suitable algorithm for identifying the OBF model parameters is applied. Specifically, the orthogonality of OBF models does not only lead to improved model efficiency (as pointed out in previous work), but also leads to improved model scalability and model stability. Its appealing scalability property derives from a previously unexplored interpretation of the OBF model as an approximation to a solution of the inhomogeneous acoustic wave equation. Following this interpretation, a novel identification algorithm is proposed that takes advantage of the OBF model orthogonality to deliver efficient, scalable, and stable OBF model estimates, which is not necessarily the case for nonlinear estimation techniques that are normally applied.

Many acoustic signal enhancement applications require adaptive filters with a long impulse response, but with a small number of filter parameters. Fixed-poles infinite impulse response (IIR) adaptive filters based on orthonormal basis functions (OBFs) present advantages over finite impulse response filters and other IIR filters, assuring stability and fast global convergence in the adaptation of the filter parameters. A scalable algorithm is introduced for the estimation of the poles of an adaptive OBF filter from multichannel input-output data. The set of poles, common to all the acoustic channels considered, is estimated in parallel to the adaptation of the linear filter parameters. It will be shown that the result of the identification with common poles is quite robust to variations in the room transfer function, suggesting the possibility that poles may be kept fixed after estimation.