Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas

This research project assesses and proposes different strategies for noise reduction based on the undecimated discrete wavelet transform to process noisy bioacoustic signals. The proposed strategies are based on different criteria for estimating the level of noise present in the signal to perform a...

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Autores:: Gómez Echavarría, Alejandro

Tipo de recurso:

Fecha de publicación:: 2018

Institución:: Universidad de San Buenaventura

Repositorio:: Repositorio USB

Idioma:: spa

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network_acronym_str	SANBUENAV2
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dc.title.spa.fl_str_mv	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
title	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
spellingShingle	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas Transformada wavelet Wavelet denoising Bioacústica Wavelet transform Bioacoustic Acústica Ruidos biológicos Procesamiento de señales Análisis de señales Procesamiento digital de señales Tratamiento digital de señales Transformada de wavelet Algoritmos Sistemas de procesamiento de audio Audio Restauración de audio
title_short	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
title_full	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
title_fullStr	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
title_full_unstemmed	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
title_sort	Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas
dc.creator.fl_str_mv	Gómez Echavarría, Alejandro
dc.contributor.advisor.none.fl_str_mv	Ugarte Macías, Juan Pablo Ugarte Macías, Juan Pablo
dc.contributor.author.none.fl_str_mv	Gómez Echavarría, Alejandro
dc.subject.spa.fl_str_mv	Transformada wavelet Wavelet denoising Bioacústica Wavelet transform Bioacoustic
topic	Transformada wavelet Wavelet denoising Bioacústica Wavelet transform Bioacoustic Acústica Ruidos biológicos Procesamiento de señales Análisis de señales Procesamiento digital de señales Tratamiento digital de señales Transformada de wavelet Algoritmos Sistemas de procesamiento de audio Audio Restauración de audio
dc.subject.lemb.spa.fl_str_mv	Acústica Ruidos biológicos Procesamiento de señales Análisis de señales Procesamiento digital de señales Tratamiento digital de señales Transformada de wavelet Algoritmos Sistemas de procesamiento de audio Audio Restauración de audio
description	This research project assesses and proposes different strategies for noise reduction based on the undecimated discrete wavelet transform to process noisy bioacoustic signals. The proposed strategies are based on different criteria for estimating the level of noise present in the signal to perform a soft thresholding on the deatail coefficients obtained from the wavelet decomposition. The proposed algorithms, called LAstd, LDstd and LDmad, use the standard deviation of the last approximation, the standard deviation of each detail at each level and the median absolute deviation to estimate the noise level respectively. The strategies are compared with Stein's unbiased risk threshold estimation method (SURE) implemented with the Matlab function wden from the wavelet toolbox which uses the discrete wavelet transform. Three sets of data were used to assess the algorithms. In a first approach, the strategies were evaluated using different owl calls with different additive noise profiles and amplitudes. To test the tolerance of the algorithms to ambient noise, a data set of fragments with acoustic events of high biological activity were extracted from field recordings. Finally, the algorithms were tested with the full recordings of the Colombian ecosystems which contained the acoustic events. The results were quantified using the signal-to-noise ratio and the spectral entropy. To complement the results, a visual analysis of the spectrograms of the processed signals was made. The methodology LAstd obtained an excellent performance when processing the owl calls contaminated with white Gaussian noise, however it is not tolerant to colored noise and narrow band noise. On the other hand, the methodologies LDstd and LDmad show a better performance in signals with colored noise, demonstrating their ability to process field recordings with ambient noise. The implementation of a smoothing parameter “q” for the thresholding in the proposed methodologies allowed to adjust the processing to avoid the loss of important information, giving to the algorithms versatility to perform well in different scenarios
publishDate	2018
dc.date.accessioned.none.fl_str_mv	2018-09-25T15:52:39Z
dc.date.available.none.fl_str_mv	2018-09-25T15:52:39Z
dc.date.issued.none.fl_str_mv	2018
dc.date.submitted.none.fl_str_mv	2018-09-25
dc.type.spa.fl_str_mv	Trabajo de grado - Pregrado
dc.type.coar.fl_str_mv	http://purl.org/coar/resource_type/c_7a1f
dc.type.spa.spa.fl_str_mv	Trabajo de Grado
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/bachelorThesis
dc.identifier.citation.spa.fl_str_mv	A. Gómez Echavarría, “Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas”, Trabajo de grado Ingeniería de Sonido, Universidad de San Buenaventura Medellín, Facultad de Ingeniería, 2018.
dc.identifier.uri.none.fl_str_mv	http://hdl.handle.net/10819/6203
identifier_str_mv	A. Gómez Echavarría, “Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas”, Trabajo de grado Ingeniería de Sonido, Universidad de San Buenaventura Medellín, Facultad de Ingeniería, 2018.
url	http://hdl.handle.net/10819/6203
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.rights.coar.fl_str_mv	http://purl.org/coar/access_right/c_abf2
dc.rights.cc.spa.fl_str_mv	Atribución-NoComercial-SinDerivadas 2.5 Colombia
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-nd/2.5/co/
rights_invalid_str_mv	Atribución-NoComercial-SinDerivadas 2.5 Colombia http://creativecommons.org/licenses/by-nc-nd/2.5/co/ http://purl.org/coar/access_right/c_abf2
dc.format.spa.fl_str_mv	pdf
dc.format.extent.spa.fl_str_mv	57 páginas
dc.format.medium.spa.fl_str_mv	Recurso en linea
dc.format.mimetype.spa.fl_str_mv	application/pdf
dc.publisher.faculty.spa.fl_str_mv	Ingenierias
dc.publisher.program.spa.fl_str_mv	Ingeniería de Sonido
dc.publisher.sede.spa.fl_str_mv	Medellín
institution	Universidad de San Buenaventura
dc.source.bibliographicCitation.spa.fl_str_mv	[1] J. B. Alonso et al., “Automatic anuran identification using noise removal and audio activity detection,” Expert Syst. Appl., vol. 72, pp. 83–92, Apr. 2017. [2] C. Bedoya, C. Isaza, J. M. Daza, and J. D. López, “Automatic recognition of anuran species based on syllable identification,” Ecol. Inform., vol. 24, pp. 200–209, 2014. [3] J. Xie, M. Towsey, J. Zhang, and P. Roe, “Image Processing and Classification Procedure for the Analysis of Australian Frog Vocalisations,” in Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval - EMR ’15, 2015, pp. 15–20. [4] B. Krause, “Anatomy of the Soundscape : Evolving Perspectives,” JAES, vol. 56, no. 1, pp. 73–80, 2008. [5] R. Bardeli, D. Wolff, F. Kurth, M. Koch, K.-H. Tauchert, and K.-H. Frommolt, “Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring,” Pattern Recognit. Lett., vol. 31, no. 12, pp. 1524–1534, 2010. [6] J. G. Colonna, M. Cristo, M. Salvatierra, and E. F. Nakamura, “An incremental technique for real-time bioacoustic signal segmentation,” Expert Syst. Appl., vol. 42, no. 21, pp. 7367– 7374, 2015. [7] H. Jaafar and D. A. Ramli, “Automatic syllables segmentation for frog identification system,” in 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, 2013, pp. 224–228. [8] T. M. Ventura et al., “Audio parameterization with robust frame selection for improved bird identification,” Expert Syst. Appl., vol. 42, no. 22, pp. 8463–8471, 2015. [9] R. Wielgat, T. P. Zielinski, T. Potempa, A. Lisowska-Lis, and D. Król, “HFCC based recognition of bird species,” Signal Process. Algorithms, Archit. Arrange. Appl. SPA 2007, pp. 129–134, 2007. [10] A. Patti and G. A. Williamson, “Methods for classification of nocturnal migratory bird vocalizations using Pseudo Wigner-Ville Transform,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 758–762. [11] M. Towsey,J. Wimmer, I. Williamson, and P. Roe, “The use of acoustic indices to determine avian species richness in audio-recordings of the environment,” Ecol. Inform., vol. 21, pp. 110–119, 2014. [12] I. Potamitis, S. Ntalampiras, O. Jahn, and K. Riede, “Automatic bird sound detection in long real-field recordings: Applications and tools,” Appl. Acoust., vol. 80, pp. 1–9, 2014. [13] R. Wielgat, P. Swietojanski, T. Potempa, and D. Krol, “On using prefiltration in HMMbased bird species recognition,” in 2012 International Conference on Signals and Electronic Systems (ICSES), 2012, pp. 1–5. [14] N. Priyadarshani, S. Marsland, I. Castro, and A. Punchihewa, “Birdsong Denoising Using Wavelets,” Plos One, vol. 11, no. 1, pp. 1–26, 2016. [15] Y. Ren, M. T. Johnson, and J. Tao, “Perceptually Motivated Wavelet Packet Transform for Bioacoustic Signal Enhancement,” J. Acoust. Soc. Am., vol. 124, no. 1, pp. 316–327, 2008. [16] A. S. Lewis and G. Knowles, “Image compression using the 2-D wavelet transform,” IEEE Trans. Image Process., vol. 1, no. 2, pp. 244–250, 1992 [17] P. Varady, “Wavelet-based adaptive denoising of phonocardiographic records,” 2001 Conf. Proc. 23rd Annu. Int. Conf. IEEE Eng. Med. Biol. Soc., vol. 2, pp. 1846–1849, 2001. [18] D. Heric and D. Zazula, “Combined edge detection using wavelet transform and signal registration,” Image Vis. Comput., vol. 25, no. 5, pp. 652–662, 2007. [19] D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994. [21] A. Cohen, I. Daubechies, B. Jawerth, and P. Vial, “Multiresolution analysis, wavelets and fast algorithms on an interval,” Comptes Rendus l Académie des Sci. - Ser. I - Math., vol. 316, no. 5, pp. 417–421, 1993. [22] M. Aminghafari, N. Cheze, and J.-M. Poggi, “Multivariate denoising using wavelets and principal component analysis,” Comput. Stat. Data Anal., vol. 50, no. 9, pp. 2381–2398, 2006. [23] R. Yang and M. Ren, “Wavelet denoising using principal component analysis,” Expert Syst. Appl., vol. 38, no. 1, pp. 1073–1076, 2011. [24] R. Patil, “Noise Reduction using Wavelet Transform and Singular Vector Decomposition,” Procedia Comput. Sci., vol. 54, pp. 849–853, Jan. 2015. [25] A. E. Cetin and M. Tofighi, “Projection-Based Wavelet Denoising [Lecture Notes],” IEEE Signal Process. Mag., vol. 32, no. 5, pp. 120–124, Sep. 2015. [26] M. Srivastava, C. L. Anderson, and J. H. Freed, “A New Wavelet Denoising Method for Selecting Decomposition Levels and Noise Thresholds,” IEEE Access, vol. 4, pp. 3862– 3877, 2016. [27] L. Jing-Yi, L. Hong, Y. Dong, and Z. Yan-Sheng, “A New Wavelet Threshold Function and Denoising Application,” Math. Probl. Eng., vol. 2016, pp. 1–8, 2016. [28] Y. Ghanbari and M. R. Karami-Mollaei, “A new approach for speech enhancement based on the adaptive thresholding of the wavelet packets,” Speech Commun., vol. 48, no. 8, pp. 927–940, 2006. [29] M. A. Ben Messaoud and A. Bouzid, “Speech Enhancement Based on Wavelet Transform and Improved Subspace Decomposition,” JAES, vol. 63, no. 12, pp. 990–1000, 2016. [30] B. M. Gur and C. Niezrecki, “Autocorrelation based denoising of manatee vocalizations using the undecimated discrete wavelet transform,” J. Acoust. Soc. Am., vol. 122, no. 1, pp. [31] I. Daubechies, “The wavelet transform, time-frequency localization and signal analysis,” IEEE Trans. Inf. Theory, vol. 36, no. 5, pp. 961–1005, 1990. [32] S. G. Mallat and Z. Zhang, “Matching pursuits with time-frequency dictionaries,” IEEE Trans. Signal Process., vol. 41, no. 12, pp. 3397–3415, 1993. [33] M. Akay and C. Mello, “Wavelets for biomedical signal processing,” Proc. 19th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. ’Magnificent Milestones Emerg. Oppor. Med. Eng., vol. 6, pp. 2688–2691, 1997. [34] Q. Jiang, “Orthogonal Multiwavelets with Optimum Time–Frequency Resolution,” IEEE Trans. Signal Process., vol. 46, no. 4, pp. 830–844, 1998. [35] S. Qian and D. Chen, “Joint time-frequency analysis,” IEEE Signal Process. Mag., vol. 16, no. 2, pp. 52–67, 1999. [36] B. Ergen, “Signal and Image Denoising Using Wavelet Transform,” in Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology, D. Baleanu, Ed. Rijeka: IntechOpen, 2012, pp. 496–514. [37] S. G. Mallat, “A Theory for Multiresolution Signal Decomposition: The Wavelet Representation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 11, no. 7, pp. 674–693, 1989. [38] O. Rioul and M. Vetterli, “Wavelets and Signal Processing,” IEEE Signal Processing Magazine, vol. 8, no. 4, pp. 14–38, 1991. [39] S. Mallat, A Wavelet Tour of Signal Processing: The Sparse Way, 3rd ed. Burlington: Academic Press, 2009. [40] H. Zou and A. H. Tewfik, “Parametrization of Compactly Supported Orthonormal Wavelets,” IEEE Trans. Signal Process., vol. 41, no. 3, pp. 1428–1431, 1993. [41] T. Blu, “A new design algorithm for two-band orthonormal rational filter banks and orthonormal rational wavelets,” IEEE Trans. Signal Process., vol. 46, no. 6, pp. 1494–1504, 1998. [42] M. Lang, H. Guo, J. E. Odegard, and C. S. Burrus, “Noise reduction using an undecimated discrete wavelet transform,” IEEE Signal Process. Lett., vol. 3, no. 1, pp. 10–12, 1996. [43] M. Jansen, Noise Reduction by Wavelet Thresholding. Heverlee, Belgium: Springer, 2001. [44] G. Beylkin, R. Coifman, and V. Rokhlin, “Fast Wavelet Transforms and Numerical Algorithms I,” Commun. Pure Appl. Math., vol. 44, no. 2, pp. 141–183, 1991. [45] D. L. Fugal, Conceptuall Wavelets In Digital Signal Processing. Space & Signals Technologies, 2009. [46] S. V. Vaseghi, Advanced Digital Signal Processing and Noise Reduction, 4th ed. Chichester: Wiley, 2009 [47] C. Shannon, “Claude Shannon,” Inf. Theory, vol. 3, p. 224, 1948. [48] D. Wang, D. Miao, and C. Xie, “Best basis-based wavelet packet entropy feature extraction and hierarchical EEG classification for epileptic detection,” Expert Syst. Appl., vol. 38, no. 11, pp. 14314–14320, 2011. [49] F. Chang, W. Hong, T. Zhang, J. Jing, and X. Liu, “Research on Wavelet Denoising for Pulse Signal Based on Improved Wavelet Thresholding,” in 2010 First International Conference on Pervasive Computing, Signal Processing and Applications, 2010, pp. 564– 567 [50] S. Blanco, A. Garay, and D. Coulombie, “Comparison of frequency bands using spectral entropy for epileptic seizure prediction,” ISRN Neurol., vol. 2013, no. 287327, 2013.
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spelling	Comunidad Científica y AcadémicaUgarte Macías, Juan Pablo59f2010c-d265-4d45-81cd-0905ab7fced9-1Ugarte Macías, Juan Pablovirtual::2371-1Gómez Echavarría, Alejandrod5272203-8a5c-4f18-802f-c3a380ff9071-12018-09-25T15:52:39Z2018-09-25T15:52:39Z20182018-09-25This research project assesses and proposes different strategies for noise reduction based on the undecimated discrete wavelet transform to process noisy bioacoustic signals. The proposed strategies are based on different criteria for estimating the level of noise present in the signal to perform a soft thresholding on the deatail coefficients obtained from the wavelet decomposition. The proposed algorithms, called LAstd, LDstd and LDmad, use the standard deviation of the last approximation, the standard deviation of each detail at each level and the median absolute deviation to estimate the noise level respectively. The strategies are compared with Stein's unbiased risk threshold estimation method (SURE) implemented with the Matlab function wden from the wavelet toolbox which uses the discrete wavelet transform. Three sets of data were used to assess the algorithms. In a first approach, the strategies were evaluated using different owl calls with different additive noise profiles and amplitudes. To test the tolerance of the algorithms to ambient noise, a data set of fragments with acoustic events of high biological activity were extracted from field recordings. Finally, the algorithms were tested with the full recordings of the Colombian ecosystems which contained the acoustic events. The results were quantified using the signal-to-noise ratio and the spectral entropy. To complement the results, a visual analysis of the spectrograms of the processed signals was made. The methodology LAstd obtained an excellent performance when processing the owl calls contaminated with white Gaussian noise, however it is not tolerant to colored noise and narrow band noise. On the other hand, the methodologies LDstd and LDmad show a better performance in signals with colored noise, demonstrating their ability to process field recordings with ambient noise. The implementation of a smoothing parameter “q” for the thresholding in the proposed methodologies allowed to adjust the processing to avoid the loss of important information, giving to the algorithms versatility to perform well in different scenariosEste proyecto de investigación evalúa y propone diferentes estrategias de reducción de ruido basadas en la transformada discreta wavelet no diezmada para procesar señales bioacústicas ruidosas. Las estrategias propuestas se basan en diferentes criterios de estimación del nivel de ruido presente en la señal para realizar un softh thresholding sobre los coeficientes de detalle obtenidos de la descomposición wavelet. Los algoritmos propuestos, denominados LAstd, LDstd y LDmad, usan la desviación estándar de la última aproximación, la desviación estándar de cada nivel de detalle y la desviación media absoluta para estimar el nivel de ruido respectivamente. Las estrategias son comparadas con el método de estimación de threshold de riesgo imparcial de Stein (SURE) implementado con la función de Matlab wden del Wavelet Toolbox, la cual usa la transformada discreta wavelet. Para probar los algoritmos se usaron tres conjuntos de datos. En un primer acercamiento se evaluaron las estrategias con distintos cantos de búhos con diferentes perfiles de ruido aditivo y amplitudes. Para probar la tolerancia de los algoritmos al ruido ambiente, se usó otro conjunto de datos formado por fragmentos de señales grabadas en campo con eventos acústicos de alta actividad biológica. Finalmente, se probaron los algoritmos con las grabaciones completas de los ecosistemas colombianos los cuales contenían los eventos acústicos. Los resultados obtenidos fueron cuantificados usando la relación señal a ruido y la entropía espectral. Para complementar los resultados se hizo un análisis visual de los espectrogramas de las señales procesadas. La metodología LAstd obtuvo un excelente desempeño al procesar los cantos de búhos contaminados con ruido Gaussiano blanco, sin embargo, no es tolerante a ruido de color y de banda angosta. Por otro lado, las metodologías LDstd y LDmad demuestran un mejor desempeño en señales con ruido de color, demostrando su capacidad para procesar señales grabadas en campo con ruido ambiente. La implementación de un parámetro “q” de suavizado del thresholding en las metodologías propuestas permitió ajustar el procesamiento para evitar la pérdida de información importante, otorgando a los algoritmos versatilidad para desempeñarse bien en distintos escenariospdf57 páginasRecurso en lineaapplication/pdfA. Gómez Echavarría, “Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas”, Trabajo de grado Ingeniería de Sonido, Universidad de San Buenaventura Medellín, Facultad de Ingeniería, 2018.http://hdl.handle.net/10819/6203spaIngenieriasIngeniería de SonidoMedellín[20] D. L. Donoho, “De-noising by soft-thresholding,” IEEE Trans. Inf. Theory, vol. 41, no. 3, pp. 613–627, May 1995.Atribución-NoComercial-SinDerivadas 2.5 ColombiaPor medio de este formato manifiesto mi voluntad de AUTORIZAR a la Universidad de San Buenaventura, Sede Bogotá, Seccionales Medellín, Cali y Cartagena, la difusión en texto completo de manera gratuita y por tiempo indefinido en la Biblioteca Digital Universidad de San Buenaventura, el documento académico-investigativo objeto de la presente autorización, con fines estrictamente educativos, científicos y culturales, en los términos establecidos en la Ley 23 de 1982, Ley 44 de 1993, Decisión Andina 351 de 1993, Decreto 460 de 1995 y demás normas generales sobre derechos de autor. Como autor manifiesto que el presente documento académico-investigativo es original y se realiza sin violar o usurpar derechos de autor de terceros, por lo tanto, la obra es de mi exclusiva autora y poseo la titularidad sobre la misma. La Universidad de San Buenaventura no será responsable de ninguna utilización indebida del documento por parte de terceros y será exclusivamente mi responsabilidad atender personalmente cualquier reclamación que pueda presentarse a la Universidad. Autorizo a la Biblioteca Digital de la Universidad de San Buenaventura convertir el documento al formato que el repositorio lo requiera (impreso, digital, electrónico o cualquier otro conocido o por conocer) o con fines de preservación digital. Esta autorización no implica renuncia a la facultad que tengo de publicar posteriormente la obra, en forma total o parcial, por lo cual podrá, dando aviso por escrito con no menos de un mes de antelación, solicitar que el documento deje de estar disponible para el público en la Biblioteca Digital de la Universidad de San Buenaventura, así mismo, cuando se requiera por razones legales y/o reglas del editor de una revista.http://creativecommons.org/licenses/by-nc-nd/2.5/co/http://purl.org/coar/access_right/c_abf2[1] J. B. Alonso et al., “Automatic anuran identification using noise removal and audio activity detection,” Expert Syst. Appl., vol. 72, pp. 83–92, Apr. 2017.[2] C. Bedoya, C. Isaza, J. M. Daza, and J. D. López, “Automatic recognition of anuran species based on syllable identification,” Ecol. Inform., vol. 24, pp. 200–209, 2014.[3] J. Xie, M. Towsey, J. Zhang, and P. Roe, “Image Processing and Classification Procedure for the Analysis of Australian Frog Vocalisations,” in Proceedings of the 2nd International Workshop on Environmental Multimedia Retrieval - EMR ’15, 2015, pp. 15–20.[4] B. Krause, “Anatomy of the Soundscape : Evolving Perspectives,” JAES, vol. 56, no. 1, pp. 73–80, 2008.[5] R. Bardeli, D. Wolff, F. Kurth, M. Koch, K.-H. Tauchert, and K.-H. Frommolt, “Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring,” Pattern Recognit. Lett., vol. 31, no. 12, pp. 1524–1534, 2010.[6] J. G. Colonna, M. Cristo, M. Salvatierra, and E. F. Nakamura, “An incremental technique for real-time bioacoustic signal segmentation,” Expert Syst. Appl., vol. 42, no. 21, pp. 7367– 7374, 2015.[7] H. Jaafar and D. A. Ramli, “Automatic syllables segmentation for frog identification system,” in 2013 IEEE 9th International Colloquium on Signal Processing and its Applications, 2013, pp. 224–228.[8] T. M. Ventura et al., “Audio parameterization with robust frame selection for improved bird identification,” Expert Syst. Appl., vol. 42, no. 22, pp. 8463–8471, 2015.[9] R. Wielgat, T. P. Zielinski, T. Potempa, A. Lisowska-Lis, and D. Król, “HFCC based recognition of bird species,” Signal Process. Algorithms, Archit. Arrange. Appl. SPA 2007, pp. 129–134, 2007.[10] A. Patti and G. A. Williamson, “Methods for classification of nocturnal migratory bird vocalizations using Pseudo Wigner-Ville Transform,” in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 758–762.[11] M. Towsey,J. Wimmer, I. Williamson, and P. Roe, “The use of acoustic indices to determine avian species richness in audio-recordings of the environment,” Ecol. Inform., vol. 21, pp. 110–119, 2014.[12] I. Potamitis, S. Ntalampiras, O. Jahn, and K. Riede, “Automatic bird sound detection in long real-field recordings: Applications and tools,” Appl. Acoust., vol. 80, pp. 1–9, 2014.[13] R. 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Coulombie, “Comparison of frequency bands using spectral entropy for epileptic seizure prediction,” ISRN Neurol., vol. 2013, no. 287327, 2013.Universidad de San Buenaventura - MedellínBiblioteca USB Medellín (San Benito) CD-4876tBiblioteca Digital Universidad de San BuenaventuraTransformada waveletWavelet denoisingBioacústicaWavelet transformBioacousticAcústicaRuidos biológicosProcesamiento de señalesAnálisis de señalesProcesamiento digital de señalesTratamiento digital de señalesTransformada de waveletAlgoritmosSistemas de procesamiento de audioAudioRestauración de audioIngeniero de SonidoEvaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicasTrabajo de grado - PregradoTrabajo de Gradoinfo:eu-repo/semantics/bachelorThesishttp://purl.org/coar/resource_type/c_7a1fPublicationhttps://scienti.minciencias.gov.co/cvlac/visualizador/generarCurriculoCv.do?cod_rh=0001472217virtual::2371-1https://scholar.google.com/citations?user=LFPIimIAAAAJ&hl=es&oi=aovirtual::2371-10000-0001-8008-3528virtual::2371-124880263-63b0-4bd4-af81-d7ff9fa6e112virtual::2371-124880263-63b0-4bd4-af81-d7ff9fa6e112virtual::2371-1ORIGINALBioacustica_Wavelet_Denoising_Gomez_2018.pdfBioacustica_Wavelet_Denoising_Gomez_2018.pdfapplication/pdf2413113https://bibliotecadigital.usb.edu.co/bitstreams/839484b2-af1f-4dd4-b766-01376d53f668/downloadb6857e6b24e43e8522a1d0f567dfea29MD51LICENSElicense.txtlicense.txttext/plain; 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Evaluación de técnicas de reducción de ruido basadas en wavelets orientadas al procesamiento de señales bioacústicas

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