Embedded wavelet analysis of non-audible signals

Analisis de integrada wavelet de señales no audibles

  • Andrés Camilo Ussa Caycedo Universidad Militar Nueva Granada
  • Olga Lucía Ramos Sandova Universidad Militar Nueva Granada
  • Darío Amaya Hurtado Universidad Militar Nueva Granada
  • Jorge Enrique Saby Beltrán Universidad Distrital Francisco José de Caldas
Palabras clave: Communication Aids for Disabled, Esophageal, Phonetics, Speech, Wavelet Analysis. (en_US)
Palabras clave: Communication Aids for Disabled, Esophageal, Phonetics, Speech, Wavelet Analysis. (es_ES)

Resumen (en_US)

The analysis of non-audible signals has gain a significant importance due to its many fields of application, among them, speech synthesis for people with speech disabilities. This analysis can be used to acquire information from the vocal apparatus without the need of speaking in order to produce a phonetic expression. The analysis of a Wavelet transformation of Spanish words recorded through a non-audible murmur microphone in order to achieve an embedded silent speech recognition system of Spanish language is proposed. A non-audible murmur microphone is used as sensor of non-vocal speech. Coding of the input data is done through a Wavelet transform using a fourth-order Daubechies function. The acquisition, processing and transmission system is applied through a STM32F4-Discovery evaluation board. The vocabulary utilized consists of command words aimed to control mobile robots or human-machine interfaces. The Wavelet transformation of four Spanish words, each of them having five independent samples, was accomplished. An analysis of the resulting data was performed, and features as average, peaks and frequency were distinguished. The processing of the signals is performed successfully and further work in speech activity detection and features classifiers is proposed.


Resumen (es_ES)

El análisis de señales no audibles ha ganado una importancia significativa debido a sus muchos campos de aplicación, entre ellos, la síntesis del habla para personas con discapacidades del habla. Este análisis puede usarse para obtener información del aparato vocal sin la necesidad de hablar para producir una expresión fonética. Se propone el análisis de una transformación Wavelet de palabras en español grabadas a través de un micrófono de murmullo no audible para lograr un sistema integrado de reconocimiento de voz silenciosa del idioma español. Se usa un micrófono de soplo no audible como sensor de habla no vocal. La codificación de los datos de entrada se realiza a través de una transformación Wavelet utilizando una función Daubechies de cuarto orden. El sistema de adquisición, procesamiento y transmisión se aplica a través de una placa de evaluación STM32F4-Discovery. El vocabulario utilizado consiste en palabras de comando destinadas a controlar robots móviles o interfaces hombre-máquina. Se logró la transformación Wavelet de cuatro palabras en español, cada una de ellas con cinco muestras independientes. Se realizó un análisis de los datos resultantes y se distinguieron características como promedio, picos y frecuencia. El procesamiento de las señales se realiza con éxito y se propone un trabajo adicional en la detección de la actividad del habla y clasificadores de características.


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Biografía del autor/a

Andrés Camilo Ussa Caycedo, Universidad Militar Nueva Granada

Mechatronics Engineer, Research Assistant, Universidad Militar Nueva Granada, Engineering Faculty Mechatronics Engineering Program, Bogotá. 

Olga Lucía Ramos Sandova, Universidad Militar Nueva Granada

Electronics Engineer, Electronics Instrumentation Specialization, Master in Teleinformatics Universidad Militar Nueva Granada, Engineering Faculty-Mechatronics Engineering Program, Bogotá. 

Darío Amaya Hurtado, Universidad Militar Nueva Granada

Electronics Engineer, Specialization in Industrial Process Automation, Master in Teleinformatics, Mechanical Engineering PhD., Universidad Militar Nueva Granada, Engineering Faculty Mechatronics Engineering Program, Bogotá.

Jorge Enrique Saby Beltrán, Universidad Distrital Francisco José de Caldas

Bachelor Degree in Linguistics and Literature, Specialization in n Semiotics, master in Spanish Linguistics, Linguistics and letter PhD., Science
of Education PhD., Universidad Distrital Francisco José de Caldas, Bogotá. 


Anami, B.S., Pagi, V.B., An acoustic signature based neural net-work model for type recognition of two-wheelers., International Multimedia, Signal Processing and Communication Technologies (IMPACT), 2009, pp.28, 31, 14-16.

Babani, D., Toda, T., Saruwatari, H., Shikano, K., Acoustic model training for non-audible murmur recognition using transformed normal speech data., IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, pp. 5224-5227.

Choi, S., Shin, Y., Park, H.K., Selection of wavelet packet measures for insufficiency murmur identification., Expert Syst Appl, Vol. 38, No. 4, 2011, pp. 4264–4271.

de Vos, J.P., Blanckenberg, M., Automated pediatric cardiac auscultation., IEEE Trans Biomed Eng, Vol. 54, No. 2, 2007, pp. 244–252.

Denby, B., Schultz, T., Honda, K., Hueber, T., Gilbert, J.M., Brumberg, J.S., Silent speech interfaces., Speech Communication, Vol. 52, No. 4, 2010, pp. 270–287.

FreeRTOS. Why use an rtos? [Internet]. [cited 2013 June 6] Available from: http://www.freertos.org/FAQWhat.html#WhyUseRTOSFAQWhat.html

Junhong, D., Ming, Y., Analysis of guided wave signal based on LabVIEW and STFT., International Conference on Computer, Mechatronics, Control and Electronic Engineering (CMCE), 2010, Vol. 5, pp. 115, 117, 24-26.

Laplante, P., Ovaska, M., Real-Time Systems Design and Analysis: Tools for the Practitioner., 4th ed., Wiley-IEEE Press, 2011, pp. 5-8.

Lee, D., Yamamoto, A., Wavelet analysis: Theory and applications., Hewlett Packard journal 45, 1994, pp. 44–45.

Lopez-Larraz, E., Mozos, O.M., Antelis, J.M., Minguez, J., Syllable-based speech recognition using EMG., Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2010, pp. 4699–4702.

Madishetty, S.K., Madanayake, A., Cintra, R.J., Dimitrov, V.S., Mugler, D.H., VLSI Architectures for the 4-Tap and 6-Tap 2-D Daubechies Wavelet Filters Using Algebraic Integers., IEEE Transactions on Circuits and Systems I: Regular Papers, Vol. 60, No. 6, 2013, pp. 1455-1468.

National Instruments. What is a real-time operating system (rtos)? (2012) [Internet]. [cited 2013 June 4] Available from: http://www.ni.com/white-paper/3938/en/pdf

Pattanaburi, K., Onshaunjit, J., Srinonchat, J., Enhancement Pattern Analysis Technique for Voiced/Unvoiced Classification., International Symposium on Computer, Consumer and Control (IS3C), 2012, pp. 389, 392, 4-6.

Press, W.H., Teukolsky, A., Vetterling, W.T., Flannery, B.P., Numerical recipes in C: the art of scientific computing., 2nd ed., New York, NY, USA, Cambridge University Press, 1992, pp. 591-602.

Satiyan, M., Hariharan, M., Nagarajan, R., Comparison of performance using daubechies wavelet family for facial expression recognition. 6th International Colloquium on Signal Processing and Its Applications, 2010, pp. 1–5.

Suresh, H.N., Balasubramanyam, V., Wavelet transforms and neural network approach for epileptical EEG., IEEE 3rd International Advance Computing Conference, 2013, pp. 12–17.

Toda, T., Nakamura, K., Nagai, T., Kaino, T., Nakajima, Y., Shikano, K., Technologies for processing body-conducted speech detected with nonaudible murmur microphone., Annual Conference of the International Speech Communication Association, 2009, pp. 632–635.

Torres-García, A., Reyes-García, C., Villaseñor-Pineda, L., Toward a silent speech interface based on unspoken speech., BIOSIGNALS, SciTePress, 2012, pp. 370-373.

Verma, A., Cabrera, S., Mayorga, A., Nazeran, H., A robust algorithm for derivation of heart rate variability spectra from ECG and PPG signals., 29th Southern Biomedical Engineering Conference, 2013, pp. 35–36.

Cómo citar
Ussa Caycedo, A. C., Ramos Sandova, O. L., Hurtado, D. A., & Saby Beltrán, J. E. (2014). Analisis de integrada wavelet de señales no audibles. Tecnura, 18, 51-60. https://doi.org/10.14483/22487638.9242
Publicado: 2014-12-01