Acoustic Impulsive Noise Based on Non-Gaussian Models: An Experimental Evaluation.

acoustic channel maximum likelihood noise estimation non-Gaussian noise

Journal

Sensors (Basel, Switzerland)
ISSN: 1424-8220
Titre abrégé: Sensors (Basel)
Pays: Switzerland
ID NLM: 101204366

Informations de publication

Date de publication:
25 Jun 2019
Historique:
received: 05 04 2019
revised: 23 05 2019
accepted: 29 05 2019
entrez: 28 6 2019
pubmed: 28 6 2019
medline: 28 6 2019
Statut: epublish

Résumé

In general, acoustic channels are not Gaussian distributed neither are second-order stationary. Considering them for signal processing methods designed for Gaussian assumptions is inadequate, consequently yielding in poor performance of such methods. This paper presents an analysis for audio signal corrupted by impulsive noise using non-Gaussian models. Audio samples are compared to the Gaussian, α -stable and Gaussian mixture models, evaluating the fitting by graphical and numerical methods. We discuss fitting properties as the window length and the overlap, finally concluding that the α -stable model has the best fit for all tested scenarios.

Identifiants

pubmed: 31242554
pii: s19122827
doi: 10.3390/s19122827
pmc: PMC6631147
pii:
doi:

Types de publication

Journal Article

Langues

eng

Subventions

Organisme : Coordenação de Aperfeiçoamento de Pessoal de Nível Superior
ID : 001

Auteurs

Danilo Pena (D)

Department of Electrical Engineering, Federal University of Rio Grande do Norte, 59078-970 Natal, Brazil. danilo@dca.ufrn.br.

Carlos Lima (C)

Department of Communications Engineering, Federal University of Rio Grande do Norte, 59078-970 Natal, Brazil. carloslima@ufrn.edu.br.

Matheus Dória (M)

Department of Communications Engineering, Federal University of Rio Grande do Norte, 59078-970 Natal, Brazil. matheusf@ufrn.edu.br.

Luan Pena (L)

Department of Electrical Engineering, Federal University of Rio Grande do Norte, 59078-970 Natal, Brazil. luan.gppcom@gmail.com.

Allan Martins (A)

Department of Electrical Engineering, Federal University of Rio Grande do Norte, 59078-970 Natal, Brazil. allan@dca.ufrn.br.

Vicente Sousa (V)

Department of Communications Engineering, Federal University of Rio Grande do Norte, 59078-970 Natal, Brazil. vicente.sousa@ufrn.edu.br.

Classifications MeSH