Modelling of acoustic transfer functions for echo cancellers

Gudvangen, S.; Flockton, S.J.

doi:10.1049/ip-vis:19951559

Cited by 6 publications

(4 citation statements)

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“…Then, the (MoorePenrose) pseudoinverse [33] of is given by where the map is defined by Proof: From a property of pseudoinverses, we have (22) Now, for all and , hence (23) and the result follows by putting (23) and Lemma 1 into (22). Below, we use Lemma 3 to prove (9), (10), and (11).…”

Section: Appendix Proofsmentioning

confidence: 99%

“…Proof of (11): Let be the subspace of subband models whose support is defined by the matrices and , and let be the projection onto . Consider the map defined by .…”

Section: Appendix Proofsmentioning

confidence: 99%

“…The advantage of this approach is that it approximates an LTI system with a very large impulse responses without implementation latency. However, a large number of poles and zeros may be needed to achieve a small approximation error, hence, it is often less numerically efficient than segmented FFT methods [10], [11]. Also, the coefficients of a pole-zero model are sensitive to quantization errors [1,Ch.…”

Section: Introductionmentioning

confidence: 99%

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A Recursive Method for the Approximation of LTI Systems Using Subband Processing

Marelli

2010

IEEE Trans. Signal Process.

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Using the subband technique, an LTI system can be implemented by the composition of an analysis filterbank, followed by a transfer matrix (subband model) and a synthesis filterbank. The advantage of this approach is that it offers a good tradeoff between latency and computational complexity. In this paper we propose an optimization method for approximating an LTI system using the subband technique. The proposed method includes optimal allocation of parameters from different FIR entries of the subband model, while keeping constant the total number of parameters, for a better utilization of the available coefficients. The optimization is done in a weighted least-squares sense considering either linear or logarithmic amplitude scale. Simulation results demonstrate the advantages of the proposed method when compared with classical implementation approaches using pole-zero transfer functions or segmented FFT algorithms.Index Terms-Modeling, system analysis and design, subband signal processing.

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Section: Appendix Proofsmentioning

confidence: 99%

“…Proof of (11): Let be the subspace of subband models whose support is defined by the matrices and , and let be the projection onto . Consider the map defined by .…”

Section: Appendix Proofsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Recursive Method for the Approximation of LTI Systems Using Subband Processing

Marelli

2010

IEEE Trans. Signal Process.

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“…Typical all-pole model orders required for approximating RTFs are in the range 50 ≤ P ≤ 500 [32]. Mourjopoulos and Paraskevas [32] state that all-pole model orders are typically a factor of 40 lower than all-zero model orders, while several studies [33,34] state that the gain achieved using pole-zero over all-zero modelling of reverberant environments is not as high as generally thought throughout the literature. A significant advantage of the all-pole model over other LTI models is its lower sensitivity to changes in source and observer positions.…”

Section: A All-pole Model For Room Acousticsmentioning

confidence: 99%

Blind single channel deconvolution using nonstationary signal processing

Hopgood

Rayner

2003

IEEE Trans. Speech Audio Process.

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Blind deconvolution is fundamental in signal processing applications and, in particular, the single channel case remains a challenging and formidable problem. This paper considers single channel blind deconvolution in the case where the degraded observed signal may be modelled as the convolution of a nonstationary source signal with a stationary distortion operator. The important feature that the source is nonstationary while the channel is stationary facilitates the unambiguous identification of either the source or channel, and deconvolution is possible, whereas if the source and channel are both stationary, identification is ambiguous. The parameters for the channel are estimated by modelling the source as a time-varying AR process and the distortion by an all-pole filter, and using the Bayesian framework for parameter estimation. This estimate can then be used to deconvolve the observed signal. In contrast to the classical histogram approach for estimating the channel poles, where the technique merely relies on the fact that the channel is actually stationary rather than modelling is as so, the proposed Bayesian method does take account for the channel's stationarity in the model and, consequently, is more robust. The properties of this model is investigated, and the advantage of utilising the nonstationarity of a system rather than considering it as a curse is discussed.

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References

2001

Signal Processing for Active Control

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Modelling of acoustic transfer functions for echo cancellers

Cited by 6 publications

References 0 publications

A Recursive Method for the Approximation of LTI Systems Using Subband Processing

A Recursive Method for the Approximation of LTI Systems Using Subband Processing

Blind single channel deconvolution using nonstationary signal processing

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