Wavelet packet transform and multilayer perceptron to identify voices with a mild degree of vocal deviation

Transformada Wavelet packet y Perceptrón Multicapa para identificación de voces con grado leve de desvío vocal

Published 2022-03-08

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Vol. 4 No. 1 (2022)
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Research Article
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Wavelet packet transform and multilayer perceptron to identify voices with a mild degree of vocal deviation. Rev. Investig. Innov. Cienc. Salud [Internet]. 2022 Mar. 8 [cited 2024 Nov. 21];4(1):16-25. Available from: https://riics.info/index.php/RCMC/article/view/126
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https://doi.org/10.46634/riics.126
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Mateus Morikawa
Departamento de Engenharia Elétrica; Universidade Tecnológica Federal do Paraná; Cornélio Procópio; Brasil.
    https://orcid.org/0000-0002-4809-1867
    Danilo Hernane Spatti
    Departamento de Sistemas de Computação; Universidade de São Paulo; São Carlos; Brasil.
      https://orcid.org/0000-0003-4613-4509
      María Eugenia Dajer
      Departamento de Engenharia Elétrica; Universidade Tecnológica Federal do Paraná; Cornélio Procópio; Brasil.
        https://orcid.org/0000-0003-1648-3919
        Show authors biography

        Introduction. Laryngeal disorders are characterized by a change in the vibratory pattern of the vocal folds. This disorder may have an organic origin described by anatomical fold modification, or a functional origin caused by vocal abuse or misuse. The most common diagnostic methods are performed by invasive imaging features that cause patient discomfort. In addition, mild voice deviations do not stop the individual from using their voices, which makes it difficult to identify the problem and increases the possibility of complications.

        Aim. For those reasons, the goal of the present paper was to develop a noninvasive alternative for the identification of voices with a mild degree of vocal deviation applying the Wavelet Packet Transform (WPT) and Multilayer Perceptron (MLP), an Artificial Neural Network (ANN).

        Methods. A dataset of 74 audio files were used. Shannon energy and entropy measures were extracted using the Daubechies 2 and Symlet 2 families and then the processing step was performed with the MLP ANN.

        Results. The Symlet 2 family was more efficient in its generalization, obtaining 99.75% and 99.56% accuracy by using Shannon energy and entropy measures, respectively. The Daubechies 2 family, however, obtained lower accuracy rates: 91.17% and 70.01%, respectively.

        Conclusion. The combination of WPT and MLP presented high accuracy for the identification of voices with a mild degree of vocal deviation.

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