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SMCS: Mobile Model Oriented to Cloud for the Automatic Classification of Environmental Sounds

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Advances in Emerging Trends and Technologies (ICAETT 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1066))

Abstract

This paper presents SMCS, a cloud-oriented mobile system model that uses a Convolutional Neural Network for the automatic classification of environmental sounds in real time. The model comprises an architectural schema with its corresponding deployment scheme in Google cloud services provider. Finally, the validation protocol of SMCS is applied in two experiments using respectively the base of free sounds FSDkaggle2018 and a selection of warning sounds extracted from the same sound base. The results of the validation of the model are promising with high values of precision in the classification of sounds, demonstrating that the SMCS model is expected to be a point of reference for the development of sound analysis systems, contributing to improving the quality of life of people with Hearing Impairment.

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Authors and Affiliations

  1. Universidad Técnica Particular de Loja, Loja, Ecuador

    María José Mora-Regalado, Omar Ruiz-Vivanco & Alexandra Gonzalez-Eras

Authors
  1. María José Mora-Regalado
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  2. Omar Ruiz-Vivanco
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  3. Alexandra Gonzalez-Eras
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Corresponding author

Correspondence to Omar Ruiz-Vivanco .

Editor information

Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Universidad Técnica de Babahoyo, Babahoyo, Ecuador

    Joffre León-Acurio

  3. Universitat de Valencia, Valencia, Valencia, Spain

    Angela Díaz Cadena

  4. Centro de Investigación y Desarrollo Profesional, Babahoyo, Los Rios Province, Ecuador

    Práxedes Montiel Díaz

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Mora-Regalado, M.J., Ruiz-Vivanco, O., Gonzalez-Eras, A. (2020). SMCS: Mobile Model Oriented to Cloud for the Automatic Classification of Environmental Sounds. In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_43

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