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Feature Set Optimisation for Infant Cry Classification

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Advances in Artificial Intelligence - IBERAMIA 2018 (IBERAMIA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11238))

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Abstract

This work deals with the development of features for the automatic classification of infant cry, considering three categories: neutral, fussing and crying vocalisations. Mel-frequency cepstral coefficients, together with standard functional obtained from these, have long been the most widely used features for all kind of speech-related tasks, including infant cry classification. However, recent works have introduced alternative filter banks leading to performance improvements and increased robustness. In this work, the optimisation of a filter bank is proposed for feature extraction and two other spectrum-based feature sets are compared. The first set of features is obtained through the optimisation of filter banks, by means of an evolutionary algorithm, in order to find a more suitable speech representation for the infant cry classification. Moreover, the classification performance of the optimised representation combined with other spectral features based on the mean log-spectrum and auditory spectrum is evaluated. The results show that these feature sets are able to improve the performance for the cry classification task.

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Notes

  1. 1.

    Neural Systems Lab., Institutes for Systems Research, UMCP. http://www.isr.umd.edu/Labs/NSL/.

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Acknowledgements

The authors wish to thank the support of the Agencia Nacional de Promoción Científica y Tecnológica (with PICT 2015-0977), the Universidad Nacional de Litoral (with CAI+D 50020150100055LI, CAI+D 50020150100059LI, CAI+D 50020150100042LI), and the Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) from Argentina.

Author information

Authors and Affiliations

  1. Research Institute for Signals, Systems and Computational Intelligence (sinc(i)), Facultad de Ingeniería y Cs. Hídricas, Universidad Nacional del Litoral CC217, Ciudad Universitaria, Paraje El Pozo, S3000, Santa Fe, Argentina

    Leandro D. Vignolo, Enrique Marcelo Albornoz & César Ernesto Martínez

  2. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Leandro D. Vignolo & Enrique Marcelo Albornoz

  3. Laboratorio de Cibernética, Facultad de Ingeniería, Universidad Nacional de Entre Ríos, Entre Ríos, Argentina

    César Ernesto Martínez

Authors
  1. Leandro D. Vignolo
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  2. Enrique Marcelo Albornoz
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  3. César Ernesto Martínez
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Corresponding author

Correspondence to Leandro D. Vignolo .

Editor information

Editors and Affiliations

  1. Universidad Nacional del Sur, Bahía Blanca, Buenos Aires, Argentina

    Guillermo R. Simari

  2. University of Madeira, Funchal, Portugal

    Eduardo Fermé

  3. Universidad Nacional de Piura, Castilla-Piura, Peru

    Flabio Gutiérrez Segura

  4. Universidad Nacional de Trujillo, Trujillo, Peru

    José Antonio Rodríguez Melquiades

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Vignolo, L.D., Albornoz, E.M., Martínez, C.E. (2018). Feature Set Optimisation for Infant Cry Classification. In: Simari, G., Fermé, E., Gutiérrez Segura, F., Rodríguez Melquiades, J. (eds) Advances in Artificial Intelligence - IBERAMIA 2018. IBERAMIA 2018. Lecture Notes in Computer Science(), vol 11238. Springer, Cham. https://doi.org/10.1007/978-3-030-03928-8_37

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  • DOI: https://doi.org/10.1007/978-3-030-03928-8_37

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