Parametric modeling is used in all those acoustic signal enhancement applications that require to model and identify a room impulse response (RIR) in a compact yet accurate way. Fixed-pole models based on orthonormal basis functions (OBFs) provide advantages over all-zero and pole-zero models. The parameters of an OBF model can be estimated from a measured target RIR by a scalable matching pursuit algorithm called OBF-MP. However, a measurement for the RIR is usually not available, and the model parameters should be estimated from input-output data. This paper introduces a block-based version of OBF-MP for the modeling and identification of room acoustic systems, which represents an intermediate step towards a sample-based recursive implementation of the algorithm. Simulation results show modeling capabilities comparable with the original OBF-MP.
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