Publication:
MFCC in audio signal processing for voice disorder: a review

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dc.citedby6
dc.contributor.authorSidhu M.S.en_US
dc.contributor.authorLatib N.A.A.en_US
dc.contributor.authorSidhu K.K.en_US
dc.contributor.authorid56259597000en_US
dc.contributor.authorid57224502095en_US
dc.contributor.authorid57225456188en_US
dc.date.accessioned2025-03-03T07:47:31Z
dc.date.available2025-03-03T07:47:31Z
dc.date.issued2024
dc.description.abstractVoice Disorder or Dysphonia has caught the attention of audio signal process engineers and researchers. The efficiency of several feature extraction and classifier implementation techniques in identifying voice abnormalities has been investigated. Mel-Frequency Cepstral Coefficient (MFCC) has been extensively used as a feature extractor. This paper adopts a Comparative Review Method to assess the effectiveness of feature extraction and classifier methods in detecting voice disorders. By examining the pairing of the Mel-Frequency Cepstral Coefficient (MFCC) with various classifiers, including Support Vector Machine (SVM), Artificial Neural Network (ANN), Decision Tree (DT), and other online or commercial classifiers, the study aims to review the robustness of MFCC in this context. The study also recognizes the significance of choosing the right database in light of the various aetiologies of pathological illnesses and its possible influence on the efficacy of voice disorder detection. ? The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.en_US
dc.description.natureArticle in pressen_US
dc.identifier.doi10.1007/s11042-024-19253-1
dc.identifier.scopus2-s2.0-85191709609
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85191709609&doi=10.1007%2fs11042-024-19253-1&partnerID=40&md5=a6d83c6f0b1d6eebf6e3246f2e172474
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/37103
dc.publisherSpringeren_US
dc.sourceScopus
dc.sourcetitleMultimedia Tools and Applications
dc.subjectClassification (of information)
dc.subjectDecision trees
dc.subjectExtraction
dc.subjectFeature extraction
dc.subjectNeural networks
dc.subjectSpeech communication
dc.subjectSpeech recognition
dc.subjectAudio signal
dc.subjectFeature classifiers
dc.subjectFeature extractor
dc.subjectFeatures extraction
dc.subjectImplementation techniques
dc.subjectMel frequency cepstral co-efficient
dc.subjectMel-frequency cepstral coefficients
dc.subjectSupport vectors machine
dc.subjectVoice disorders
dc.subjectSupport vector machines
dc.titleMFCC in audio signal processing for voice disorder: a reviewen_US
dc.typeArticleen_US
dspace.entity.typePublication
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