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Nonperiodic Pathologic Voice Signals Classification Using Mel-Spectrogram and VGGish

In: Health Technologies and Demographic Challenges

Author

Listed:
  • Joana Filipa Teixeira Fernandes

    (Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), - Instituto Politécnico de Bragança (IPB)
    Faculty of Engineering of University of Porto (FEUP))

  • João Viana Pinto

    (University Hospital Centre of São João, Otorhinolaryngology Department
    University of Oporto, Faculty of Medicine, Unit of Otorhinolaryngology, Department of Surgery and Physiology
    Centre for Health Technology and Services Research (CINTESIS) Rua Dr. Plácido da Costa)

  • Carla Pinto Moura

    (University of Porto, Genetics, Faculty of Medicine, Department of Pathology
    University Hospital Centre of São JoãoPorto, Department of Otorhinolaryngology)

  • Helena Vilarinho

    (University Hospital Centre of São JoãoPorto, Department of Otorhinolaryngology
    School of Health Sciences (ESSUA), University of Aveiro)

  • Felipe Teixeira

    (Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), - Instituto Politécnico de Bragança (IPB)
    Applied Management Research Unit (UNIAG) - Instituto Politécnico de Bragança (IPB)
    School of Sciences and TechnologyUniversity of Trás-os-Montes and Alto Douro (UTAD) Quinta de Prados, Engineering Department)

  • Diamantino Freitas

    (Faculty of Engineering of University of Porto (FEUP))

  • João Paulo Teixeira

    (Research Centre in Digitalization and Intelligent Robotics (CeDRI), Laboratório para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), - Instituto Politécnico de Bragança (IPB))

Abstract

In this work and the literature, voice signals can be classified as periodic (type 1) or either some periodicity (type 2) and chaos (type 3). This work aims to classify signs into types 1, 2 or 3 to be subsequently applied in a classification system for pathological/control signs. The original dataset is composed of 466 type 1 individuals, 900 type 2 individuals, and 84 type 3 individuals classified by an otolaryngologist. 15% of the data was used for testing and the remaining 85% was used for training and validation. A data augmentation technique was applied to balance the data in training set. Therefore, for the test set, 3380 sounds were used, 1020 type 1, 1280 type 2 and 1080 type 3. Of these, 80% were used for training and 20% for validation. The Mel spectrograms of the signals were used in the input of a VGGish to retrain the model in classifying the 3 types of signals. Regarding test accuracy, this network obtained 71.2%.

Suggested Citation

  • Joana Filipa Teixeira Fernandes & João Viana Pinto & Carla Pinto Moura & Helena Vilarinho & Felipe Teixeira & Diamantino Freitas & João Paulo Teixeira, 2025. "Nonperiodic Pathologic Voice Signals Classification Using Mel-Spectrogram and VGGish," Springer Proceedings in Business and Economics, in: Pedro Miguel Gaspar & Juan Manuel Cueva Lovelle & Carlos Mentenegro-Marín & Teresa Guarda (ed.), Health Technologies and Demographic Challenges, pages 3-13, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-94901-2_1
    DOI: 10.1007/978-3-031-94901-2_1
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