Sparse Array-Based Room Transfer Function Estimation for Echo Cancellation

Yellepeddi, Atulya; Florêncio, Dinei

doi:10.1109/lsp.2013.2297832

Cited by 6 publications

(5 citation statements)

References 21 publications

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“…There is also no sparsity to speak of if only a single microphone is used (if an array of microphones is used, then it is possible to use a sparse basis to represent signal structure [187]). There appears to be little easily exploitable structure in this problem, other than the input structure.…”

Section: Algorithms and Resultsmentioning

confidence: 99%

“…Examples abound in communication [142], room acoustics [187], underwater communication [38], systems biology [95], seismic tomography [7], quantitative spectroscopy [65] and many others [167]. With such a vast variety of possible applications, the space of algorithms and solutions is also huge.…”

Section: Linear Time-invariant System Identificationmentioning

confidence: 99%

“…In recent years, the field has seen a flurry of research involving the use of sparse solution techniques in fields as diverse as underwater communication and networks [18], [116], [153], room acoustics [187], sparse inverse covariance matrix estimation [27], [66], [114], and source localization [105].…”

Section: Subset Selection and Lasso Regressionmentioning

confidence: 99%

“…While many modern applications such as gaming systems controlled by voice like the Microsoft XBox [152] or in-car audio [36] use a microphone array rather than a single microphone, and can then exploit array geometry to obtain accurate estimates of the room impulse response [61], [187], the size and cost of an array sometimes make it necessary to use a single microphone [164].…”

Section: Room Impulse Response Estimation For Acoustic Echo Cancellationmentioning

confidence: 99%

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Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

Self Cite

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Identifying and tracking an unknown linear system from observations of its inputs and outputs is a problem at the heart of many different applications. Due to the complexity and rapid variability of modern systems, there is extensive interest in solving the problem with as little data and computation as possible.This thesis introduces the novel approach of reducing problem dimension by exploiting statistical structure on the input. By modeling the input to the system of interest as a graph-structured random process, it is shown that a large parameter identification problem can be reduced into several smaller pieces, making the overall problem considerably simpler.Algorithms that can leverage this property in order to either improve the performance or reduce the computational complexity of the estimation problem are developed. The first of these, termed the graphical expectation-maximization least squares (GEM-LS) algorithm, can utilize the reduced dimensional problems induced by the structure to improve the accuracy of the system identification problem in the low sample regime over conventional methods for linear learning with limited data, including regularized least squares methods.Next, a relaxation of the GEM-LS algorithm termed the relaxed approximate graph structured least squares (RAGS-LS) algorithm is obtained that exploits structure to perform highly efficient estimation. The RAGS-LS algorithm is then recast into a recursive framework termed the relaxed approximate graph structured recursive least squares (RAGS-RLS) algorithm, which can be used to track time-varying linear systems with low complexity while achieving tracking performance comparable to much more computationally intensive methods.The performance of the algorithms developed in the thesis in applications such as channel identification, echo cancellation and adaptive equalization demonstrate that the gains admitted by the graph framework are realizable in practice. The methods have wide applicability, and in particular show promise as the estimation and adaptation algorithms for a new breed of fast, accurate underwater acoustic modems.The contributions of the thesis illustrate the power of graphical model structure in simplifying difficult learning problems, even when the target system is not directly structured.Thesis Supervisor: James C. Preisig Title: Scientist Emeritus, Woods Hole Oceanographic Institution AcknowledgmentsPiglet noticed that even though he had a Very Small Heart, it could hold a rather large amount of Gratitude. Winnie the PoohA.A.MilneMy years at MIT have been about so much more than a degree. In the past six years you have introduced to more worlds than I ever knew existed. As this long and wonderful journey ends, I find that my heart can, too, hold so much gratitude, for everything that you have given.Of course, I haven't said who "you" is, so here goes.It is impossible to write any list of acknowledgements without having at the very top my thesis advisor Dr. James Preisig, who has truly been a wide-angle lens f...

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Section: Algorithms and Resultsmentioning

confidence: 99%

Section: Linear Time-invariant System Identificationmentioning

confidence: 99%

Section: Subset Selection and Lasso Regressionmentioning

confidence: 99%

Section: Room Impulse Response Estimation For Acoustic Echo Cancellationmentioning

confidence: 99%

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Graphical model driven methods in adaptive system identification

Yellepeddi¹

2016

Self Cite

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“…In 2014, an algorithm is given for a more accurate estimates of the impulse response between each loudspeaker and microphone by Yellepeddi, A. and Florencio, D. [22]. The algorithm has array structure and it takes the sparsity of the reflections arriving at the array to give improved performance in echo cancellation applications, using both synthetic and real data …”

Section: Review On Acoustic Echo Cancellation Systemmentioning

confidence: 99%

Review Paper on Linear and Nonlinear Acoustic Echo Cancellation

Gupta¹,

Gupta²,

Chandra³

2015

Advances in Intelligent Systems and Computing

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Abstract. In this paper a review on acoustic echo cancellation (AEC) systems based on linear AEC and nonlinear AEC are presented. The paper covers advancements in the previous research works related to the acoustic echo cancellation process. Here review is done based on adaptive algorithms used. Since in linear AEC systems, the non-linearity caused by loudspeakers, amplifier and low-quality enclosures are not taken into considerations. The implementation of linear AEC is based on the assumption of linearity. The performance of echo cancellation algorithms are degraded due to non-linearities in the acoustic path. Therefore non-linear AEC systems are evaluated and reviewed along with linear AEC.

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Uncalibrated 3D room geometry estimation from sound impulse responses

Crocco

Trucco

2017

Journal of the Franklin Institute

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Sparse Array-Based Room Transfer Function Estimation for Echo Cancellation

Cited by 6 publications

References 21 publications

Graphical model driven methods in adaptive system identification

Graphical model driven methods in adaptive system identification

Review Paper on Linear and Nonlinear Acoustic Echo Cancellation

Uncalibrated 3D room geometry estimation from sound impulse responses

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