TY - JOUR
T1 - A scalable algorithm for physically motivated and sparse approximation of room impulse responses with orthonormal basis functions
AU - Vairetti, Giacomo
AU - De Sena, Enzo
AU - Catrysse, Michael
AU - Jensen, Soren Holdt
AU - Moonen, Marc
AU - van Waterschoot, Toon
PY - 2017/7/1
Y1 - 2017/7/1
N2 - 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.
AB - 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.
KW - Matching pursuit
KW - orthonormal basis function models
KW - parametric modeling
KW - room acoustics
UR - http://www.scopus.com/inward/record.url?scp=85020019415&partnerID=8YFLogxK
U2 - 10.1109/TASLP.2017.2700940
DO - 10.1109/TASLP.2017.2700940
M3 - Journal article
AN - SCOPUS:85020019415
SN - 1558-7916
VL - 25
SP - 1547
EP - 1561
JO - I E E E Transactions on Audio, Speech and Language Processing
JF - I E E E Transactions on Audio, Speech and Language Processing
IS - 7
M1 - 7918506
ER -