A room impulse response (RIR) characterizes the sound propagation between a source and a microphone placed in a room. Accurate modeling of an RIR is essential in many acoustic signal processing applications, and precise modeling of a RIR using a small number of parameters is often required. Modeling is a challenge because room responses may have tens of thousands of taps that vary greatly when the source and microphone locations are slightly changed. In this paper, a subband multichannel method for accurate modeling of long RIRs is proposed, which is computationally efficient and robust against RIR variations. A dual-tree complex wavelet packet transform is utilized to decompose a multichannel RIR into aliasing-free subband signals. Low-order adaptive Kautz filters are designed to model subband signals using the acoustical poles common to the RIR channels. A least-squares algorithm is introduced to efficiently estimate the common acoustic poles (CP) in each subband. The algorithm precisely estimates the CPs after a low number of iterations, and unconditionally guarantees the stability of the estimated poles. Experimental results demonstrate that the proposed method accurately models the room responses while exhibiting robustness against room response variations caused by changing the source and microphone locations.
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