Robust modeling of acoustic paths using a sparse adaptive algorithm

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dc.contributor.author Maheshwari, Jyoti
dc.contributor.author George, Nithin V.
dc.date.accessioned 2015-09-13T10:55:07Z
dc.date.available 2015-09-13T10:55:07Z
dc.date.issued 2016-03
dc.identifier.citation Maheshwari, Jyoti and George, Nithin V., “Robust modeling of acoustic paths using a sparse adaptive algorithm”, Applied Acoustics, DOI: 10.1016/j.apacoust.2015.08.013, vol. 101, pp. 122-126, Mar. 2016.
dc.identifier.issn 0003682X
dc.identifier.uri dx.doi.org/10.1016/j.apacoust.2015.08.013
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/1912
dc.description.abstract Acoustic impulse response functions are generally sparse in nature and traditionally these are modeled by adaptive finite impulse response (FIR) filters trained using a least mean square (LMS) algorithm. The conventional LMS algorithm is not effective in modeling sparse systems and sparse LMS algorithms have been recently developed to improve the modeling in such scenarios. However, the traditional sparse LMS algorithms are not robust to disturbances at the error sensor and may diverge in some scenarios. With an objective to overcome this limitation of conventional sparse adaptive algorithm, this paper presents a robust sparse adaptive algorithm. The new algorithm has been shown to effectively model sparse systems in a robust manner. In addition, the algorithm has been successfully applied in modeling the acoustic feedback path in a behind the ear digital hearing aid. en_US
dc.description.statementofresponsibility by Jyoti Maheshwari and Nithin V. George
dc.format.extent vol. 101, pp. 122-126
dc.language.iso en_US en_US
dc.publisher Elsevier en_US
dc.subject parse model en_US
dc.subject System identification en_US
dc.subject Robust least mean square algorithm en_US
dc.subject Hearing aid en_US
dc.subject Acoustic path en_US
dc.title Robust modeling of acoustic paths using a sparse adaptive algorithm en_US
dc.type Article en_US
dc.relation.journal Applied Acoustics


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