Transformation of electromyographic signals of subvocal speech using compressive sensing and artificial intelligence

Authors

  • José Daniel Ramírez Corzo Universidad de Pamplona
  • Luis Enrique Mendoza Universidad de Pamplona
  • Leonardo Carrascal Universidad de Pamplona

DOI:

https://doi.org/10.24054/sei.v3i1.1189

Keywords:

Sparse, Python, electromyography multi - resolution analysis, Raspberry Pi

Abstract

This article shows the acquisition of electrical signals from the nerves of the throat and oral cords, transforming them into voice signals using advanced techniques in digital signal processing and artificial intelligence, based on the extraction of patterns based on compressive sensing, Entropy, discrete Wavelet transform and a classifier based on vector support machines of least squares, once the system has been calibrated, the results showed that 95% + - 0.34 of data were correctly classified. The developed system was used in 500 signals and is based on an Open Source programming language implemented in an embedded system. Finally, it was shown that it is possible to use compressive sensing to extract subvocal speech patterns.

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Published

2020-01-20

How to Cite

Ramírez Corzo, J. D., Mendoza, L. E., & Carrascal, L. (2020). Transformation of electromyographic signals of subvocal speech using compressive sensing and artificial intelligence. Revista Semilleros De Investigación, 3(1), 1–9. https://doi.org/10.24054/sei.v3i1.1189

Issue

Vol. 3 No. 1 (2020): January - June

Section

Artículos

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Copyright (c) 2020 SEMILLEROS DE INVESTIGACIÓN

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