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Determination of harmonic parameters in pathological voices-efficient algorithm

2023Journal article Open access
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dc.contributor.authorFernandes, Joana Filipa Teixeira
dc.contributor.authorFreitas, Diamantino Silva
dc.contributor.authorCandido Junior, Arnaldo
dc.contributor.authorTeixeira, João Paulo
dc.date.accessioned2022-02-21T16:21:25Z
dc.date.available2022-02-21T16:21:25Z
dc.date.issued2023
dc.description.abstractFeatured Application The paper describes a low-complexity/efficient algorithm to determine the short-term Autocorrelation, HNR, and NHR in sustained vowel audios, to be used in stand-alone devices with low computational power. These parameters can be used as input features of a smart medical decision support system for speech pathology diagnosis. The harmonic parameters Autocorrelation, Harmonic to Noise Ratio (HNR), and Noise to Harmonic Ratio are related to vocal quality, providing alternative measures of the harmonic energy of a speech signal. They will be used as input resources for an intelligent medical decision support system for the diagnosis of speech pathology. An efficient algorithm is important when implementing it on low-power devices. This article presents an algorithm that determines these parameters by optimizing the window type and length. The method used comparatively analyzes the values of the algorithm, with different combinations of window and size and a reference value. Hamming, Hanning, and Blackman windows with lengths of 3, 6, 12, and 24 glottal cycles and various sampling frequencies were investigated. As a result, we present an efficient algorithm that determines the parameters using the Hanning window with a length of six glottal cycles. The mean difference of Autocorrelation is less than 0.004, and that of HNR is less than 0.42 dB. In conclusion, this algorithm allows extraction of the parameters close to the reference values. In Autocorrelation, there are no significant effects of sampling frequency. However, it should be used cautiously for HNR with lower sampling rates.pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationFernandes, Joana Filipa Teixeira; Freitas, Diamantino Silva; Candido Junior, Arnaldo; Teixeira, João Paulo (2023). Determination of harmonic parameters in pathological voices-efficient algorithm. Applied. eISSN 2076-3417. 13:4, p. 1-21pt_PT
dc.identifier.doi10.3390/app13042333
dc.identifier.eissn2076-3417
dc.identifier.urihttp://hdl.handle.net/10198/25093
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPI
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectVoice disorder parameterspt_PT
dc.subjectAutocorrelationpt_PT
dc.subjectHarmonic to noise ratiopt_PT
dc.subjectAutocorrelation algorithmpt_PT
dc.subjectHNR algorithmpt_PT
dc.subjectNoise to harmonic ratiopt_PT
dc.titleDetermination of harmonic parameters in pathological voices-efficient algorithmpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.titleAppliedpt_PT
person.familyNameFernandes
person.familyNameTeixeira
person.givenNameJoana Filipa Teixeira
person.givenNameJoão Paulo
person.identifierABC-9055-2020
person.identifier663194
person.identifier.ciencia-idAE12-440A-299D
person.identifier.ciencia-id4F15-B322-59B4
person.identifier.orcid0000-0002-0618-4627
person.identifier.orcid0000-0002-6679-5702
person.identifier.ridN-6576-2013
person.identifier.scopus-author-id57069567500
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationa6f7a119-fbc9-439f-8dd9-0bbc9ec82fad
relation.isAuthorOfPublication33f4af65-7ddf-46f0-8b44-a7470a8ba2bf
relation.isAuthorOfPublication.latestForDiscoverya6f7a119-fbc9-439f-8dd9-0bbc9ec82fad

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