Pole and zero modeling of room transfer functions

Mourjopoulos, John; Paraskevas, Michael

doi:10.1016/0022-460x(91)90764-b

Cited by 52 publications

(37 citation statements)

References 4 publications

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“…AZ models [1], for which A(r, z) = 1, can achieve an arbitrary degree of accuracy by using a high-order FIR filter. The main problem is that the number of parameters of the filter necessary to model the resonant behavior of the system often has to be quite large, depending on the sampling frequency f s and the reverberation time T .…”

Section: B Conventional Parametric Models For Room Acousticsmentioning

confidence: 99%

“…echo cancellation, feedback cancellation, and dereverberation, as well as in auralization systems. The most common parametric models are All-Zero (AZ) models [1], which define a finite impulse response (FIR) filter as a truncation of a sampled room impulse response (RIR). AZ models enable achieving an arbitrary degree of accuracy, but a good approximation of a RIR usually requires a large number of model parameters.…”

Section: Introductionmentioning

confidence: 99%

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A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

Vairetti

Sena

Catrysse

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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Abstract-Parametric modeling of room acoustics aims at representing room transfer functions (RTFs) 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.

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Section: B Conventional Parametric Models For Room Acousticsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

Vairetti

Sena

Catrysse

et al. 2017

IEEE/ACM Trans. Audio Speech Lang. Process.

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“…By taking the parameter vector θ = [a 1 a 2 · · · a n b 1 b · · · b n ] T , the well-known ARX model structure is derived [8]. Setting a k = 0, for all k, leads to FIR model structure, or by taking b k = 0, for all k, AR model structure is realized.…”

Section: Orthonormal Modelsmentioning

confidence: 99%

“…It is desirable to construct the orthonormal basis function based on this operator. The procedure of a unifying construction of orthonormal bases can be seen in [8] and the result is given by…”

Section: Orthonormal Modelsmentioning

confidence: 99%

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System Identification of Acoustic Characteristics of Enclosures With Resonant Second Order Dynamics

Chaudhry¹,

Chaudhr²

2006

PIER

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Abstract-This research concerns offline identification of acoustic characteristics of enclosures with second-order resonant dynamics and their modeling as linear dynamic systems. The applied models can be described by basis function expansions. The practical problem of acoustic echo in enclosures is used as the target problem to be addressed. It has been found out that the classical filters are ineffective filter structures for approximating an echo generating system, due to their many required parameters. In order to reduce the number of estimated parameters, alternative methods for modeling the room impulse response need to be investigated. Out of various available techniques impulse response identification is utilized. With the help of given experimental data, the enclosures' impulse response is modeled using special orthonormal basis functions called Kautz functions. As another improved approximation, hybrid multistage system identifiers have been used in which the simplicity of classical filter structures and fast convergence of orthonormal structures is utilized as an advantage.

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Fareeha transform: A new generalized Laplace transform

Khan

Khalid

2023

Math Methods in App Sciences

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In this paper, a new integral-type transformation is being introduced, which is the generalization of Laplace and Fourier transform. This integral transform is named as "Fareeha transform." It is successfully applied on ordinary differential equation to show its efficacy and simplicity. Also, the possible applications of Fareeha transform in control theory, electric circuits, and data compression have been discussed in detail.

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Pole and zero modeling of room transfer functions

Cited by 52 publications

References 4 publications

A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

A Scalable Algorithm for Physically Motivated and Sparse Approximation of Room Impulse Responses With Orthonormal Basis Functions

System Identification of Acoustic Characteristics of Enclosures With Resonant Second Order Dynamics

Fareeha transform: A new generalized Laplace transform

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