Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing
El morphing de audio sobresale entre las tecnicas de s ´ ´ıntesis y transformacion del sonido debido a su potencial creativo y su versatilidad. El morphing de audio se centra en la creación de un sonido nuevo a partir de la mezcla de atributos de un sonido fuente y un sonido objetivo. Existen multip...
- Autores:
-
Gutiérrez Arboleda, Sara
Herrera Carmona, Danilo
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2023
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/13463
- Acceso en línea:
- https://hdl.handle.net/10819/13463
- Palabra clave:
- 530 - Física::534 - Sonido y vibraciones relacionadas
Procesamiento musical
Instrumentos musicales
Sonidos sintetizados
Morphing de audio
Modelamiento sinusoidal
Síntesis de audio
Descomposición de matrices no negativas
Audio morphing
Sinusoidal modeling
Audio synthesis
Non-negative matrix decomposition
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
id |
SANBUENAV2_6bd34943a9cb18a5bce6a46b88e7fabe |
---|---|
oai_identifier_str |
oai:bibliotecadigital.usb.edu.co:10819/13463 |
network_acronym_str |
SANBUENAV2 |
network_name_str |
Repositorio USB |
repository_id_str |
|
dc.title.spa.fl_str_mv |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
title |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
spellingShingle |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing 530 - Física::534 - Sonido y vibraciones relacionadas Procesamiento musical Instrumentos musicales Sonidos sintetizados Morphing de audio Modelamiento sinusoidal Síntesis de audio Descomposición de matrices no negativas Audio morphing Sinusoidal modeling Audio synthesis Non-negative matrix decomposition |
title_short |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
title_full |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
title_fullStr |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
title_full_unstemmed |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
title_sort |
Comparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphing |
dc.creator.fl_str_mv |
Gutiérrez Arboleda, Sara Herrera Carmona, Danilo |
dc.contributor.advisor.none.fl_str_mv |
Yepes Díaz, Mateo |
dc.contributor.author.none.fl_str_mv |
Gutiérrez Arboleda, Sara Herrera Carmona, Danilo |
dc.subject.ddc.none.fl_str_mv |
530 - Física::534 - Sonido y vibraciones relacionadas |
topic |
530 - Física::534 - Sonido y vibraciones relacionadas Procesamiento musical Instrumentos musicales Sonidos sintetizados Morphing de audio Modelamiento sinusoidal Síntesis de audio Descomposición de matrices no negativas Audio morphing Sinusoidal modeling Audio synthesis Non-negative matrix decomposition |
dc.subject.other.none.fl_str_mv |
Procesamiento musical Instrumentos musicales Sonidos sintetizados |
dc.subject.proposal.spa.fl_str_mv |
Morphing de audio Modelamiento sinusoidal Síntesis de audio Descomposición de matrices no negativas |
dc.subject.proposal.eng.fl_str_mv |
Audio morphing Sinusoidal modeling Audio synthesis Non-negative matrix decomposition |
description |
El morphing de audio sobresale entre las tecnicas de s ´ ´ıntesis y transformacion del sonido debido a su potencial creativo y su versatilidad. El morphing de audio se centra en la creación de un sonido nuevo a partir de la mezcla de atributos de un sonido fuente y un sonido objetivo. Existen multiples técnicas de morphing que han ido evolucionando a lo largo de los anos, por ello, el proposito de este trabajo es construir un corpus de audios a partir dos tecnicas diferentes, una basada en el “Modelamiento sinusoidal (SMT)” y la segunda que funciona a partir de la “factorizacion de matrices no negativas (NMF)”, ambas partiendo de la misma base de datos de sonidos fuente y objetivo, esto con el fin de tener el contexto en comun para llevar a cabo dos tipos de análisis: uno objetivo basado en descriptores de audio que permitan obtener una imagen matematica y ex acta sobre el movimiento de las características sonoras de ambosmetodos y así conocer como se afecta de forma distinta el timbre en el proceso de síntesis; el segundo analisis evalúa de forma subjetiva las señales de audio a través de pruebas que exponen las características sonoras y t´ımbricas de los sonidos sintetizados a un conjunto de sujetos con el fin de obtener una imagen perceptual que acompane a los descriptores matemáticos y así tener una referencia sobre como estos sereflejan de forma psicoacustica. Por ambas técnicas, se llegó a la conclusión de que es posible observar un movimiento coherente objetiva y subjetivamente de la cualidad percibida como brillo, a su vez, losresultados del analisis arrojaron una clara predominancia del m ´ etodo SMT por encima del NMF tanto en la coherencia del comportamiento de los descriptores sonoros como la calidad general del audio y la preferencia de los sujetos de prueba. |
publishDate |
2023 |
dc.date.issued.none.fl_str_mv |
2023 |
dc.date.accessioned.none.fl_str_mv |
2024-04-19T22:40:42Z |
dc.date.available.none.fl_str_mv |
2024-04-19T22:40:42Z |
dc.type.spa.fl_str_mv |
Trabajo de grado - Pregrado |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_7a1f |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/bachelorThesis |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/TP |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_7a1f |
status_str |
acceptedVersion |
dc.identifier.citation.none.fl_str_mv |
S. Gutiérrez Arboleda & D. Herrera Carmona , ”Comparación objetiva y subjetiva de las caracter´ısticassonoras de los audios generados apartir de dos métodos diferentes de síntesis por morphing.”, Tesis de Pregrado, Ingeniería de Sonido, Universidad de San Buenaventura, Facultad de Ingenier´ıas, 2023 |
dc.identifier.instname.spa.fl_str_mv |
instname:Universidad de San Buenaventura |
dc.identifier.reponame.spa.fl_str_mv |
reponame:Repositorio Institucional Universidad de San Buenaventura |
dc.identifier.repourl.spa.fl_str_mv |
repourl:https://bibliotecadigital.usb.edu.co/ |
dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/10819/13463 |
identifier_str_mv |
S. Gutiérrez Arboleda & D. Herrera Carmona , ”Comparación objetiva y subjetiva de las caracter´ısticassonoras de los audios generados apartir de dos métodos diferentes de síntesis por morphing.”, Tesis de Pregrado, Ingeniería de Sonido, Universidad de San Buenaventura, Facultad de Ingenier´ıas, 2023 instname:Universidad de San Buenaventura reponame:Repositorio Institucional Universidad de San Buenaventura repourl:https://bibliotecadigital.usb.edu.co/ |
url |
https://hdl.handle.net/10819/13463 |
dc.language.iso.none.fl_str_mv |
spa |
language |
spa |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
rights_invalid_str_mv |
http://purl.org/coar/access_right/c_abf2 |
dc.format.extent.none.fl_str_mv |
65 páginas |
dc.format.mimetype.spa.fl_str_mv |
application/pdf |
dc.publisher.spa.fl_str_mv |
Universidad de San Buenaventura |
dc.publisher.branch.spa.fl_str_mv |
Medellín |
dc.publisher.faculty.spa.fl_str_mv |
Facultad de Ingeniería |
dc.publisher.place.none.fl_str_mv |
Medellín |
dc.publisher.program.spa.fl_str_mv |
Ingeniería de Sonido |
institution |
Universidad de San Buenaventura |
dc.source.bibliographicCitation.spa.fl_str_mv |
[1] E. Tellman, L. Haken y B. Holloway, ✭✭Timbre morphing using the lemur representation,✮✮ ICMC Proceedings, pags. 329-330, 1994. [2] W. Morinosato, K. Atsugi-shi y O. Naotoshi, ✭✭Timbre interpolation of sounds using a sinosoidal model,✮✮ ICMC Proceedings, pags. 408-411, 1995 [3] M. Slaney, M. Covell y B. Lassiter, ✭✭Automatic audio morphing,✮✮ International conference on acoustics, speech, and signal processing, mayo de 1996. [4] M. Caetano y X. Rodet, ✭✭Sound morphing by feature interpolation,✮✮ en IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, pags. 161-164. ´ DOI: 10.1109/ICASSP. 2011.5946365 [5] Zynaptiq, ✭✭MORPH 2.0, Real-Time structural audio morphing,✮✮ 2021. direccion: https://www. ´ zynaptiq.com/morph/. [6] MeldaProduction, ✭✭MMorph,✮✮ 2021. direccion: https://www.meldaproduction.com/MMorph. [7] A. van den Oord, S. Dieleman, H. Zen et al., ✭✭WaveNet: a generative model for raw audio,✮✮ CoRR, vol. 2, pags. 1-15, 2016. arXiv: 1609.03499 [8] T. L. Paine, P. Khorrami, S. Chang et al., ✭✭Fast wavenet generation algorithm,✮✮ CoRR, vol. 3, pags. 1-6, 2016. arXiv: 1611.09482 [9] M. Caetano, ✭✭Morphing musical instrument sounds with the sinusoidal model in the sound morphing toolbox,✮✮ Lecture Notes in Computer Science, pag. 10, 2021. ´ DOI: 10.1007/978-3-030-70210-6 31. [10] G. Roma, O. Green y P. A. Tremblay, ✭✭Audio morphing using matrix decomposition and optimal [11] J. M. Grey, ✭✭Multidimensional perceptual scaling of musical timbres,✮✮ The journal of the jcoustical society of America, vol. 61, n.o 5, pags. 1270-1277, mayo de 1977. ´ DOI: 10.1121/1.381428. [12] G. Peeters, B. L. Giordano, P. Susini, N. Misdariis y S. McAdams, ✭✭The Timbre Toolbox: Extracting audio descriptors from musical signals,✮✮ The journal of the acoustical society of America, vol. 130, pags. 2902-2916, 5 nov. de 2011, ´ ISSN: 0001-4966. DOI: 10.1121/1.3642604. [13] J. Cortez, M. gomez y D. Baez, ✭✭El algoritmo de la Transformada Rapida de Fourier y su contro- ´ vertido origen,✮✮ Ciencia y Desarrollo, vol. XXIV, 139 1998. [14] X. Serra y J. Smith, ✭✭A system for sound analysis/transformation/synthesis based on a deterministic plus stochastic decomposition,✮✮ Computer music journal, vol. 14, n.o 4, pags. 12-24, 1990, ´ ISSN: 01489267, 15315169. [15] M. A. Hossan, S. Memon y M. A. Gregory, ✭✭A novel approach for MFCC feature extraction,✮✮ en 2010 4th International Conference on Signal Processing and Communication Systems, vol. 4, 2010, ISBN: 9781424479078. DOI: 10.1109/ICSPCS.2010.5709752. [16] M. Covell y M. Withgott, ✭✭Spanning the gap between motion estimation and morphing,✮✮ en IEEE International Conference on Acoustics, Speech and Signal Processing, vol. v, 1994, V/213-V/216 vol.5. DOI: 10.1109/ICASSP.1994.389494. [17] M. Caetano y X. Rodet, ✭✭Automatic timbral morphing of musical instruments sounds by high-level descriptors,✮✮ International Computer Music Conference, ICMC, jun. de 2010. [18] A. Olivero, B. Torresani, P. Depalle y R. Kronland-Martinet, ´ ✭✭Sound morphing strategies based on alterations of time-frequency representations by gabor multipliers,✮✮ mar. de 2012, pags. 1-8. [19] J. H. Engel, C. Resnick, A. Roberts et al., ✭✭Neural audio synthesis of musical notes with wavenet autoencoders,✮✮ CoRR, vol. 1, abr. de 2017 [20] S. Mehri, K. Kumar, I. Gulrajani et al., ✭✭SampleRNN: An unconditional end-to-end neural audio generation model,✮✮ CoRR, vol. 1, dic. de 2016. [21] N. Kalchbrenner, E. Elsen, K. Simonyan et al., ✭✭Efficient Neural Audio Synthesis,✮✮ 2018. [22] S. Kim, S.-g. Lee, J. Song, J. Kim y S. Yoon, ✭✭FloWaveNet : A generative flow for raw audio,✮✮ CoRR, nov. de 2018. [23] S. Vasquez y M. Lewis, ✭✭MelNet: A generative model for audio in the frequency domain,✮✮ CoRR, jun. de 2019. [24] J. Nistal, S. Lattner y G. Richard, ✭✭DrumGAN: synthesis of drum sounds with timbral feature conditioning using generative adversarial networks,✮✮ CoRR, vol. 2, ago. de 2020. [25] P. Esling, N. Masuda, A. Bardet, R. Despres y A. Chemla–Romeu-Santos, ✭✭Universal audio synthesizer control with normalizing flows,✮✮ CoRR, jul. de 2019. [26] J. J. Burred, ✭✭Cross-synthesis based on spectrogram factorization,✮✮ en International Conference on Mathematics and Computing, 2013. direccion: http://hdl.handle.net/2027/spo.bbp2372.2013.032. [27] T. Henderson y J. Solomon, ✭✭Audio Transport: A generalized portamento via optimal transport,✮✮ International Conference on Digital Audio Effects, vol. 22, pags. 1-5, jun. de 2019. [28] Z. Pru˚sa, P. Balazs y P. L. Søndergaard, ˇ ✭✭A noniterative method for reconstruction of phase from STFT magnitude,✮✮ IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 25, n.o 5, pags. 1154-1164, 5 2017, ´ ISSN: 23299290. DOI: 10.1109/TASLP.2017.2678166. [29] T. G. Porcello, ✭✭The ethics of digital audio-sampling: engineers’ discourse,✮✮ Popular Music, vol. 10, n.o 1, pags. 69-84, 1991. ´ DOI: 10.1017/S0261143000004323. [30] S. McGuire y R. Pritts, Audio sampling a practical guide, Routledge, ed. Oxford: Focal Press, 2008, ISBN: 978-0-240-52073-5. DOI: https://doi.org/10.1016/B978-0-240-52073-5.50005-6. [31] P. Boulez, ✭✭Timbre and composition: timbre and language,✮✮ Contemporary music review 2, vol. 13, universidad de Alcala, ed., p ´ ags. 42-51, 1999, ´ ISSN: 1134-8615. [32] P. Laforga, ✭✭Conceptos f´ısicos de las ondas sonoras,✮✮ F´ısica y Sociedad, vol. 11, colegio oficial de f´ısicos, ed., pags. 3-6, 2000 [33] D. Luce, ✭✭Physical correlates of nonpercussive musical instrument tones,✮✮ Tesis doct., Massachusetts Institute of Technology, 1963, ISBN: 978-0-387-32576-7. [34] J. C. Risset y D. L. Wessel, ✭✭Exploration of timbre by analysis and synthesiss,✮✮ The Psychology of Music, Cognition and Perception, vol. 2, D. Deutsch, ed., pags. 113-169, 1999. [35] H. B. Lincoln, ✭✭Uses of the computer in music composition and research,✮✮ Advances in Computers, vol. 12, M. Rubtnoff, ed., pags. 73-114, 1972, ´ ISSN: 0065-2458. [36] M. Mathews, The technology of computer music. The MIT Press, 1969, ISBN: 9780262130509. [37] A. Marui y W. Martens, ✭✭Constructing individual and group timbre spaces for sharpness-matched distorted guitar timbres,✮✮ en Audio Engineering Society - 119th, vol. 3, ene. de 2005, pag. 6627. ´ direccion: http://www.aes.org/e-lib/browse.cfm?elib=13288. [38] J. C. R. Licklider, Handbook of experimental psychology, S. Stevens, ed. John Wiley Sons, 1951, vol. 4 [39] P. Cook, ✭✭Toward the perfect audio morph? singing voice synthesis and processing,✮✮ en Workshop on Digital Audio Effects, 1998. direccion: https://www.dafx.de/paper-archive/search.php?years=1998.. [40] R. Dannenberg, M. Serra y D. Rubine, ✭✭Analysis and synthesis of tones by spectral interpolation,✮✮ The Journal of the Acoustical Society of America, vol. 82, n.o 1, pags. 69-69, ago. de 2005, ´ ISSN: 0001-4966. [41] D. Williams y T. Brookes, ✭✭Perceptually-motivated Audio Morphing: Warmth,✮✮ en 128th Audio Engineering Society Convention, vol. 1, mayo de 2010, pags. 8019-8030. direcci ´ on: http://www. ´ aes.org/e-lib/browse.cfm?elib=15316 [42] S. Kazazis, P. Depalle y S. Mcadams, ✭✭Sound morphing by audio descriptors and parameter interpolation,✮✮ en International Conference on Digital Audio Effects, vol. 19, ago. de 2016, pags. 145-152. ´ direccion: http://dafx16.vutbr.cz/dafxpapers/21-DAFx-16 ´ paper 46-PN.pdf. [43] J. O. Smith, Mathematics of the Discrete Fourier Transform with audio aplications, 2.a ed. W3K Publishing, 2017, ISBN: ”978-0-9745607-4-8”. [44] T. Tolonen, ✭✭Methods for separation of harmonic sound sources using sinusoidal modeling,✮✮ en AES Convention, vol. 106, mayo de 1999, pags. 4958-4977. direcci ´ on: http://www.aes.org/e- ´ lib/browse.cfm?elib=8222 [45] D. Lee y H. S. Seung, ✭✭Learning the parts of objects by non-negative matrix factorization,✮✮ Nature, vol. 401, pags. 778-791, 6755 oct. de 1999, ´ ISSN: 00280836. [46] P. Smaragdis y J. C. Brown, ✭✭Non-negative matrix factorization for polyphonic music transcription,✮✮ en Workshop on applications of signal processing to audio and acoustics, vol. 5, sep. de 2003, pags. 177-180. ´ DOI: 10 . 1109 / ASPAA . 2003 . 1285860. direccion: https : / / ieeexplore . ieee . org / ´ document/1285860. [47] V. Pulkki, S. Delikaris-Manias y A. Politis, ✭✭Source Separation and Reconstruction of Spatial Audio Using Spectrogram Factorization,✮✮ en Parametric Time-Frequency Domain Spatial Audio. 2018, vol. 1, pags. 215-250. ´ DOI: 10.1002/9781119252634.ch9 [48] M. Caetano y X. Rodet, ✭✭Musical instrument sound morphing guided by perceptually motivated features,✮✮ IEEE Transactions on Audio, Speech and Language Processing, vol. 21, pags. 1666-1675, ´ 8 2013, ISSN: 15587916. DOI: 10.1109/TASL.2013.2260154. [49] C. Villani, Optimal Transport Old and New, 1.a ed. Springer, 2008, vol. 338. DOI: 10.1007/978-3- 540-71050-9. [50] R. Picas, P. Rodriguez, O. Hector, D. Dabiri y X. Serra, ✭✭Good-sounds.org: a framework to explore goodness in instrumental sounds,✮✮ New York: 17th International Society of Music Information Retrieval, 2016 [52] D. Fitzgerald, ✭✭Harmonic/percussive separation using median filtering,✮✮ 2010 [53] H. Qiao, ✭✭New SVD based initialization strategy for non-negative matrix factorization,✮✮ Pattern Recognition Letters, vol. 63, pags. 71-77, 2015. [54] C. D. Martin y M. A. Porter, ✭✭The extraordinary SVD,✮✮ American Mathematical Monthly, vol. 119, pags. 838-851, 10, ´ ISSN: 00029890. DOI: 10.4169/AMER.MATH.MONTHLY.119.10.838 [55] J. Neri y P. Depalle, ✭✭Fast partial tracking of audio with real-time capability through linear programming,✮✮ en Proceedings of the International Conference on Digital Audio Effects (DAFx), 2018, pags. 326-333. [56] K. R. Fitz y S. A. Fulop, ✭✭A unified theory of time-frequency reassignment,✮✮ arXiv preprint arXiv:0903.3080, 2009. [57] G. Peyre y M. Cuturi, ´ ✭✭Computational Optimal Transport: With Applications to Data Science,✮✮ Foundations and Tr [58] G. Peyre y M. Cuturi, ´ ✭✭Computational Optimal Transport: With Applications to Data Science,✮✮ Foundations and Trends® in Machine Learning, vol. 11, 5-6 2019, ISSN: 1935-8237. DOI: 10. 1561/2200000073 [60] M. Portnoff, ✭✭Magnitude-phase relationships for short-time Fourier transforms based on Gaussian analysis windows,✮✮ en ICASSP’79. IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, vol. 4, 1979, pags. 186-189. [61] A. Woodward, ✭✭Priority Queue - File Exchange - MATLAB Central,✮✮ MATLAB Central File Exchange, 2022. direccion: https://la.mathworks.com/matlabcentral/fileexchange/69142-priority- ´ queue/. [62] P. Balazs, D. Bayer, F. Jaillet y P. Sondergaard, ✭✭The phase derivative around zeros of the short-time Fourier transform,✮✮ arXiv preprint arXiv:1103.0409, 201 [63] S. Grofit e Y. Lavner, ✭✭Time-scale modification of audio signals using enhanced WSOLA with management of transients,✮✮ IEEE transactions on audio, speech, and language processing, vol. 16, n.o 1, pags. 106-115, 2007 [64] A. Camacho y J. G. Harris, ✭✭A sawtooth waveform inspired pitch estimator for speech and music,✮✮ The Journal of the Acoustical Society of America, vol. 124, pags. 1638-1652, 3 sep. de 2008, ´ ISSN: 0001-4966. DOI: 10.1121/1.2951592. [65] R. McAulay y T. Quatieri, ✭✭Speech analysis/synthesis based on a sinusoidal representation,✮✮ IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, n.o 4, pags. 744-754, 1986. [66] R. McAulay y T. Quatieri, ✭✭Speech analysis/synthesis based on a sinusoidal representation,✮✮ IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, n.o 4, pags. 744-754, 1986. [67] P. Herrera, A. Yeterian y F. Gouyon, ✭✭Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques,✮✮ en International conference on music and artificial intelligence, Springer, 2002, pags. 69-80. [68] M. R2022b, ✭✭Spectral centroid for audio signals and auditory spectrograms - MATLAB spectralCentroidMathWorks Switzerland,✮✮ MathWorks - MATLAB and Simulink Conferences - MATLAB Simulink, direccion: https://ch.mathworks.com/help/audio/ref/spectralcentroid.html. [69] D. Giannoulis, M. Massberg y J. D. Reiss, ✭✭Parameter automation in a dynamic range compressor,✮✮ Journal of the Audio Engineering Society, vol. 61, n.o 10, pags. 716-726, 2013. [70] E. Scheirer y M. Slaney, ✭✭Construction and evaluation of a robust multifeature speech/music discriminator,✮✮ en 1997 IEEE international conference on acoustics, speech, and signal processing, IEEE, vol. 2, 1997, pags. 1331-1334. [71] B. Burger, R. Ahokas, A. Keipi y P. Toiviainen, ✭✭Relationships between spectral flux, perceived rhythmic strength, and the propensity to move,✮✮ en Proceedings of the Sound and Music Computing Conferences, Logos Verlag Berlin, 2013. [72] M. R2022b, ✭✭Spectral flux for audio signals and auditory spectrograms - MATLAB spectralFluxMathWorks Switzerland,✮✮ MathWorks - MATLAB and Simulink Conferences - MATLAB Simulink., direccion: https://ch.mathworks.com/help/audio/ref/spectralflux.html. [73] Essentia, ✭✭Algorithm reference: LogAttackTime — Essentia 2.1-beta6-dev documentation,✮✮ Homepage — Essentia 2.1-beta6-dev documentation., direccion: https://essentia.upf.edu/reference/ ´ streaming LogAttackTime.html. [75] U. internacional de telecomunicaciones, ✭✭Metodos generales para la evaluaci ´ on subjetiva de la ´ calidad de sonido Serie BS Servicio de radiodifusion (sonora), ´ ✮✮ 2019. direccion: http://www.itu. ´ int/publ/R-REC/es. [76] W. Hoeg, L. Christensen y R. Walker, ✭✭Subjective assessment of audio quality-the means and methods in the EBU,✮✮ EBU Technical Review, pags. 40-50, 1997. [77] I. K. Paz, ✭✭Media aritmetica simple, ´ ✮✮ Boletın electronico ´ , vol. 7, pags. 1-13, 2007. [78] J. C. Senar, ✭✭La medicion de la repetibilidad y el error de medida, ´ ✮✮ Etologuıa, vol. 17, pags. 53-64, ´ 1999. |
dc.source.bibliographicCitation.spá.fl_str_mv |
[59] V. M. Panaretos e Y. Zemel, ✭✭Statistical aspects of wasserstein distances,✮✮ Annual Review of Statistics and Its Application, vol. 6, 2019, ISSN: 2326831X. DOI: 10.1146/annurev- statistics030718-104938. |
dc.source.bibliographicCitation.none.fl_str_mv |
[74] F. M. H. Arellano y M. B. Mexico, ´ ✭✭El Concepto de Distancia y su Aplicacion en Estadıstica ´ Multivariada,✮✮ Datos Diagnosticos y Tendencias, 1970.q |
dc.source.other.none.fl_str_mv |
Biblioteca USB Medellín (San Benito): TG-7108t |
bitstream.url.fl_str_mv |
https://bibliotecadigital.usb.edu.co/bitstreams/2752ea98-4e37-4cbf-a5d3-a06f860a17cb/download https://bibliotecadigital.usb.edu.co/bitstreams/febe29a2-6276-492b-a69a-3b1089f20a1a/download https://bibliotecadigital.usb.edu.co/bitstreams/9ea51e4f-693a-45c3-875f-a51ccadc14e9/download https://bibliotecadigital.usb.edu.co/bitstreams/3f1bf3e3-fced-4e25-964d-2b1656b46d6b/download |
bitstream.checksum.fl_str_mv |
2ecc47e264ad723334bbebe3702b2ee3 ce8fd7f912f132cbeb263b9ddc893467 9f5a7dca5edf5087732d92d7b999b388 16161ff90bce8e4e52789d6b9e352535 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 |
repository.name.fl_str_mv |
Repositorio Institucional Universidad de San Buenaventura Colombia |
repository.mail.fl_str_mv |
bdigital@metabiblioteca.com |
_version_ |
1837099295545753600 |
spelling |
Yepes Díaz, Mateob181e0ce-3d3b-4793-93ad-ecc95c04a275-1Gutiérrez Arboleda, Sara750abb31-3492-47e9-9b7d-4eea0a5892b2-1Herrera Carmona, Danilo7f0767ca-7133-448c-b4ff-8e80d9f2048a-12024-04-19T22:40:42Z2024-04-19T22:40:42Z2023El morphing de audio sobresale entre las tecnicas de s ´ ´ıntesis y transformacion del sonido debido a su potencial creativo y su versatilidad. El morphing de audio se centra en la creación de un sonido nuevo a partir de la mezcla de atributos de un sonido fuente y un sonido objetivo. Existen multiples técnicas de morphing que han ido evolucionando a lo largo de los anos, por ello, el proposito de este trabajo es construir un corpus de audios a partir dos tecnicas diferentes, una basada en el “Modelamiento sinusoidal (SMT)” y la segunda que funciona a partir de la “factorizacion de matrices no negativas (NMF)”, ambas partiendo de la misma base de datos de sonidos fuente y objetivo, esto con el fin de tener el contexto en comun para llevar a cabo dos tipos de análisis: uno objetivo basado en descriptores de audio que permitan obtener una imagen matematica y ex acta sobre el movimiento de las características sonoras de ambosmetodos y así conocer como se afecta de forma distinta el timbre en el proceso de síntesis; el segundo analisis evalúa de forma subjetiva las señales de audio a través de pruebas que exponen las características sonoras y t´ımbricas de los sonidos sintetizados a un conjunto de sujetos con el fin de obtener una imagen perceptual que acompane a los descriptores matemáticos y así tener una referencia sobre como estos sereflejan de forma psicoacustica. Por ambas técnicas, se llegó a la conclusión de que es posible observar un movimiento coherente objetiva y subjetivamente de la cualidad percibida como brillo, a su vez, losresultados del analisis arrojaron una clara predominancia del m ´ etodo SMT por encima del NMF tanto en la coherencia del comportamiento de los descriptores sonoros como la calidad general del audio y la preferencia de los sujetos de prueba.Audio morphing stands out among sound synthesis and transformation techniques because of its creative potential and versatility. Audio morphing focuses on the creation of a new sound by mixing the attributes of a source sound and a target sound. There are multiple morphing techniques that have evolved over the years, therefore, the purpose of this work is to build a corpus of audios from two different techniques, one based on the “Sinusoidal Modeling (SMT)” and another that works from the “non-negative matrix factorization (NMF)”, both starting from the same database of source and target sounds, this in order to have the context in common to carry out two types of analysis: an objective one based on audio descriptors that allow to obtain a mathematical and exact image about the movement of the sound characteristics of both methods and thus to know how the timbre is affected differently in the synthesis process; the second analysis evaluates subjectively the audio signals through tests that expose the sonorous and timbre characteristics of the synthesized sounds to a set of subjects in order to obtain a perceptual image that accompanies the mathematical descriptors and thus to have a reference on how these are reflected in a psychoacoustic way. Finally, it was concluded that it is possible to observe an objectively and subjectively coherent movement of the perceived quality as brightness, in turn, the results of the analysis showed a clear predominance of the SMT method over the NMF both in the coherence of the behavior of the sound descriptors and the general quality of the audio and the preference of the test subjects.PregradoIngeniero de Sonido65 páginasapplication/pdfS. Gutiérrez Arboleda & D. Herrera Carmona , ”Comparación objetiva y subjetiva de las caracter´ısticassonoras de los audios generados apartir de dos métodos diferentes de síntesis por morphing.”, Tesis de Pregrado, Ingeniería de Sonido, Universidad de San Buenaventura, Facultad de Ingenier´ıas, 2023instname:Universidad de San Buenaventurareponame:Repositorio Institucional Universidad de San Buenaventurarepourl:https://bibliotecadigital.usb.edu.co/https://hdl.handle.net/10819/13463spaUniversidad de San BuenaventuraMedellínFacultad de IngenieríaMedellínIngeniería de Sonidoinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2[1] E. Tellman, L. Haken y B. Holloway, ✭✭Timbre morphing using the lemur representation,✮✮ ICMC Proceedings, pags. 329-330, 1994.[2] W. Morinosato, K. Atsugi-shi y O. Naotoshi, ✭✭Timbre interpolation of sounds using a sinosoidal model,✮✮ ICMC Proceedings, pags. 408-411, 1995[3] M. Slaney, M. Covell y B. Lassiter, ✭✭Automatic audio morphing,✮✮ International conference on acoustics, speech, and signal processing, mayo de 1996.[4] M. Caetano y X. Rodet, ✭✭Sound morphing by feature interpolation,✮✮ en IEEE International Conference on Acoustics, Speech and Signal Processing, 2011, pags. 161-164. ´ DOI: 10.1109/ICASSP. 2011.5946365[5] Zynaptiq, ✭✭MORPH 2.0, Real-Time structural audio morphing,✮✮ 2021. direccion: https://www. ´ zynaptiq.com/morph/.[6] MeldaProduction, ✭✭MMorph,✮✮ 2021. direccion: https://www.meldaproduction.com/MMorph.[7] A. van den Oord, S. Dieleman, H. Zen et al., ✭✭WaveNet: a generative model for raw audio,✮✮ CoRR, vol. 2, pags. 1-15, 2016. arXiv: 1609.03499[8] T. L. Paine, P. Khorrami, S. Chang et al., ✭✭Fast wavenet generation algorithm,✮✮ CoRR, vol. 3, pags. 1-6, 2016. arXiv: 1611.09482[9] M. Caetano, ✭✭Morphing musical instrument sounds with the sinusoidal model in the sound morphing toolbox,✮✮ Lecture Notes in Computer Science, pag. 10, 2021. ´ DOI: 10.1007/978-3-030-70210-6 31.[10] G. Roma, O. Green y P. A. Tremblay, ✭✭Audio morphing using matrix decomposition and optimal[11] J. M. Grey, ✭✭Multidimensional perceptual scaling of musical timbres,✮✮ The journal of the jcoustical society of America, vol. 61, n.o 5, pags. 1270-1277, mayo de 1977. ´ DOI: 10.1121/1.381428.[12] G. Peeters, B. L. Giordano, P. Susini, N. Misdariis y S. McAdams, ✭✭The Timbre Toolbox: Extracting audio descriptors from musical signals,✮✮ The journal of the acoustical society of America, vol. 130, pags. 2902-2916, 5 nov. de 2011, ´ ISSN: 0001-4966. DOI: 10.1121/1.3642604.[13] J. Cortez, M. gomez y D. Baez, ✭✭El algoritmo de la Transformada Rapida de Fourier y su contro- ´ vertido origen,✮✮ Ciencia y Desarrollo, vol. XXIV, 139 1998.[14] X. Serra y J. Smith, ✭✭A system for sound analysis/transformation/synthesis based on a deterministic plus stochastic decomposition,✮✮ Computer music journal, vol. 14, n.o 4, pags. 12-24, 1990, ´ ISSN: 01489267, 15315169.[15] M. A. Hossan, S. Memon y M. A. Gregory, ✭✭A novel approach for MFCC feature extraction,✮✮ en 2010 4th International Conference on Signal Processing and Communication Systems, vol. 4, 2010, ISBN: 9781424479078. DOI: 10.1109/ICSPCS.2010.5709752.[16] M. Covell y M. Withgott, ✭✭Spanning the gap between motion estimation and morphing,✮✮ en IEEE International Conference on Acoustics, Speech and Signal Processing, vol. v, 1994, V/213-V/216 vol.5. DOI: 10.1109/ICASSP.1994.389494.[17] M. Caetano y X. Rodet, ✭✭Automatic timbral morphing of musical instruments sounds by high-level descriptors,✮✮ International Computer Music Conference, ICMC, jun. de 2010.[18] A. Olivero, B. Torresani, P. Depalle y R. Kronland-Martinet, ´ ✭✭Sound morphing strategies based on alterations of time-frequency representations by gabor multipliers,✮✮ mar. de 2012, pags. 1-8.[19] J. H. Engel, C. Resnick, A. Roberts et al., ✭✭Neural audio synthesis of musical notes with wavenet autoencoders,✮✮ CoRR, vol. 1, abr. de 2017[20] S. Mehri, K. Kumar, I. Gulrajani et al., ✭✭SampleRNN: An unconditional end-to-end neural audio generation model,✮✮ CoRR, vol. 1, dic. de 2016.[21] N. Kalchbrenner, E. Elsen, K. Simonyan et al., ✭✭Efficient Neural Audio Synthesis,✮✮ 2018.[22] S. Kim, S.-g. Lee, J. Song, J. Kim y S. Yoon, ✭✭FloWaveNet : A generative flow for raw audio,✮✮ CoRR, nov. de 2018.[23] S. Vasquez y M. Lewis, ✭✭MelNet: A generative model for audio in the frequency domain,✮✮ CoRR, jun. de 2019.[24] J. Nistal, S. Lattner y G. Richard, ✭✭DrumGAN: synthesis of drum sounds with timbral feature conditioning using generative adversarial networks,✮✮ CoRR, vol. 2, ago. de 2020.[25] P. Esling, N. Masuda, A. Bardet, R. Despres y A. Chemla–Romeu-Santos, ✭✭Universal audio synthesizer control with normalizing flows,✮✮ CoRR, jul. de 2019.[26] J. J. Burred, ✭✭Cross-synthesis based on spectrogram factorization,✮✮ en International Conference on Mathematics and Computing, 2013. direccion: http://hdl.handle.net/2027/spo.bbp2372.2013.032.[27] T. Henderson y J. Solomon, ✭✭Audio Transport: A generalized portamento via optimal transport,✮✮ International Conference on Digital Audio Effects, vol. 22, pags. 1-5, jun. de 2019.[28] Z. Pru˚sa, P. Balazs y P. L. Søndergaard, ˇ ✭✭A noniterative method for reconstruction of phase from STFT magnitude,✮✮ IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 25, n.o 5, pags. 1154-1164, 5 2017, ´ ISSN: 23299290. DOI: 10.1109/TASLP.2017.2678166.[29] T. G. Porcello, ✭✭The ethics of digital audio-sampling: engineers’ discourse,✮✮ Popular Music, vol. 10, n.o 1, pags. 69-84, 1991. ´ DOI: 10.1017/S0261143000004323.[30] S. McGuire y R. Pritts, Audio sampling a practical guide, Routledge, ed. Oxford: Focal Press, 2008, ISBN: 978-0-240-52073-5. DOI: https://doi.org/10.1016/B978-0-240-52073-5.50005-6.[31] P. Boulez, ✭✭Timbre and composition: timbre and language,✮✮ Contemporary music review 2, vol. 13, universidad de Alcala, ed., p ´ ags. 42-51, 1999, ´ ISSN: 1134-8615.[32] P. Laforga, ✭✭Conceptos f´ısicos de las ondas sonoras,✮✮ F´ısica y Sociedad, vol. 11, colegio oficial de f´ısicos, ed., pags. 3-6, 2000[33] D. Luce, ✭✭Physical correlates of nonpercussive musical instrument tones,✮✮ Tesis doct., Massachusetts Institute of Technology, 1963, ISBN: 978-0-387-32576-7.[34] J. C. Risset y D. L. Wessel, ✭✭Exploration of timbre by analysis and synthesiss,✮✮ The Psychology of Music, Cognition and Perception, vol. 2, D. Deutsch, ed., pags. 113-169, 1999.[35] H. B. Lincoln, ✭✭Uses of the computer in music composition and research,✮✮ Advances in Computers, vol. 12, M. Rubtnoff, ed., pags. 73-114, 1972, ´ ISSN: 0065-2458.[36] M. Mathews, The technology of computer music. The MIT Press, 1969, ISBN: 9780262130509.[37] A. Marui y W. Martens, ✭✭Constructing individual and group timbre spaces for sharpness-matched distorted guitar timbres,✮✮ en Audio Engineering Society - 119th, vol. 3, ene. de 2005, pag. 6627. ´ direccion: http://www.aes.org/e-lib/browse.cfm?elib=13288.[38] J. C. R. Licklider, Handbook of experimental psychology, S. Stevens, ed. John Wiley Sons, 1951, vol. 4[39] P. Cook, ✭✭Toward the perfect audio morph? singing voice synthesis and processing,✮✮ en Workshop on Digital Audio Effects, 1998. direccion: https://www.dafx.de/paper-archive/search.php?years=1998..[40] R. Dannenberg, M. Serra y D. Rubine, ✭✭Analysis and synthesis of tones by spectral interpolation,✮✮ The Journal of the Acoustical Society of America, vol. 82, n.o 1, pags. 69-69, ago. de 2005, ´ ISSN: 0001-4966.[41] D. Williams y T. Brookes, ✭✭Perceptually-motivated Audio Morphing: Warmth,✮✮ en 128th Audio Engineering Society Convention, vol. 1, mayo de 2010, pags. 8019-8030. direcci ´ on: http://www. ´ aes.org/e-lib/browse.cfm?elib=15316[42] S. Kazazis, P. Depalle y S. Mcadams, ✭✭Sound morphing by audio descriptors and parameter interpolation,✮✮ en International Conference on Digital Audio Effects, vol. 19, ago. de 2016, pags. 145-152. ´ direccion: http://dafx16.vutbr.cz/dafxpapers/21-DAFx-16 ´ paper 46-PN.pdf.[43] J. O. Smith, Mathematics of the Discrete Fourier Transform with audio aplications, 2.a ed. W3K Publishing, 2017, ISBN: ”978-0-9745607-4-8”.[44] T. Tolonen, ✭✭Methods for separation of harmonic sound sources using sinusoidal modeling,✮✮ en AES Convention, vol. 106, mayo de 1999, pags. 4958-4977. direcci ´ on: http://www.aes.org/e- ´ lib/browse.cfm?elib=8222[45] D. Lee y H. S. Seung, ✭✭Learning the parts of objects by non-negative matrix factorization,✮✮ Nature, vol. 401, pags. 778-791, 6755 oct. de 1999, ´ ISSN: 00280836.[46] P. Smaragdis y J. C. Brown, ✭✭Non-negative matrix factorization for polyphonic music transcription,✮✮ en Workshop on applications of signal processing to audio and acoustics, vol. 5, sep. de 2003, pags. 177-180. ´ DOI: 10 . 1109 / ASPAA . 2003 . 1285860. direccion: https : / / ieeexplore . ieee . org / ´ document/1285860.[47] V. Pulkki, S. Delikaris-Manias y A. Politis, ✭✭Source Separation and Reconstruction of Spatial Audio Using Spectrogram Factorization,✮✮ en Parametric Time-Frequency Domain Spatial Audio. 2018, vol. 1, pags. 215-250. ´ DOI: 10.1002/9781119252634.ch9[48] M. Caetano y X. Rodet, ✭✭Musical instrument sound morphing guided by perceptually motivated features,✮✮ IEEE Transactions on Audio, Speech and Language Processing, vol. 21, pags. 1666-1675, ´ 8 2013, ISSN: 15587916. DOI: 10.1109/TASL.2013.2260154.[49] C. Villani, Optimal Transport Old and New, 1.a ed. Springer, 2008, vol. 338. DOI: 10.1007/978-3- 540-71050-9.[50] R. Picas, P. Rodriguez, O. Hector, D. Dabiri y X. Serra, ✭✭Good-sounds.org: a framework to explore goodness in instrumental sounds,✮✮ New York: 17th International Society of Music Information Retrieval, 2016[52] D. Fitzgerald, ✭✭Harmonic/percussive separation using median filtering,✮✮ 2010[53] H. Qiao, ✭✭New SVD based initialization strategy for non-negative matrix factorization,✮✮ Pattern Recognition Letters, vol. 63, pags. 71-77, 2015.[54] C. D. Martin y M. A. Porter, ✭✭The extraordinary SVD,✮✮ American Mathematical Monthly, vol. 119, pags. 838-851, 10, ´ ISSN: 00029890. DOI: 10.4169/AMER.MATH.MONTHLY.119.10.838[55] J. Neri y P. Depalle, ✭✭Fast partial tracking of audio with real-time capability through linear programming,✮✮ en Proceedings of the International Conference on Digital Audio Effects (DAFx), 2018, pags. 326-333.[56] K. R. Fitz y S. A. Fulop, ✭✭A unified theory of time-frequency reassignment,✮✮ arXiv preprint arXiv:0903.3080, 2009.[57] G. Peyre y M. Cuturi, ´ ✭✭Computational Optimal Transport: With Applications to Data Science,✮✮ Foundations and Tr[58] G. Peyre y M. Cuturi, ´ ✭✭Computational Optimal Transport: With Applications to Data Science,✮✮ Foundations and Trends® in Machine Learning, vol. 11, 5-6 2019, ISSN: 1935-8237. DOI: 10. 1561/2200000073[60] M. Portnoff, ✭✭Magnitude-phase relationships for short-time Fourier transforms based on Gaussian analysis windows,✮✮ en ICASSP’79. IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, vol. 4, 1979, pags. 186-189.[61] A. Woodward, ✭✭Priority Queue - File Exchange - MATLAB Central,✮✮ MATLAB Central File Exchange, 2022. direccion: https://la.mathworks.com/matlabcentral/fileexchange/69142-priority- ´ queue/.[62] P. Balazs, D. Bayer, F. Jaillet y P. Sondergaard, ✭✭The phase derivative around zeros of the short-time Fourier transform,✮✮ arXiv preprint arXiv:1103.0409, 201[63] S. Grofit e Y. Lavner, ✭✭Time-scale modification of audio signals using enhanced WSOLA with management of transients,✮✮ IEEE transactions on audio, speech, and language processing, vol. 16, n.o 1, pags. 106-115, 2007[64] A. Camacho y J. G. Harris, ✭✭A sawtooth waveform inspired pitch estimator for speech and music,✮✮ The Journal of the Acoustical Society of America, vol. 124, pags. 1638-1652, 3 sep. de 2008, ´ ISSN: 0001-4966. DOI: 10.1121/1.2951592.[65] R. McAulay y T. Quatieri, ✭✭Speech analysis/synthesis based on a sinusoidal representation,✮✮ IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, n.o 4, pags. 744-754, 1986.[66] R. McAulay y T. Quatieri, ✭✭Speech analysis/synthesis based on a sinusoidal representation,✮✮ IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, n.o 4, pags. 744-754, 1986.[67] P. Herrera, A. Yeterian y F. Gouyon, ✭✭Automatic classification of drum sounds: a comparison of feature selection methods and classification techniques,✮✮ en International conference on music and artificial intelligence, Springer, 2002, pags. 69-80.[68] M. R2022b, ✭✭Spectral centroid for audio signals and auditory spectrograms - MATLAB spectralCentroidMathWorks Switzerland,✮✮ MathWorks - MATLAB and Simulink Conferences - MATLAB Simulink, direccion: https://ch.mathworks.com/help/audio/ref/spectralcentroid.html.[69] D. Giannoulis, M. Massberg y J. D. Reiss, ✭✭Parameter automation in a dynamic range compressor,✮✮ Journal of the Audio Engineering Society, vol. 61, n.o 10, pags. 716-726, 2013.[70] E. Scheirer y M. Slaney, ✭✭Construction and evaluation of a robust multifeature speech/music discriminator,✮✮ en 1997 IEEE international conference on acoustics, speech, and signal processing, IEEE, vol. 2, 1997, pags. 1331-1334.[71] B. Burger, R. Ahokas, A. Keipi y P. Toiviainen, ✭✭Relationships between spectral flux, perceived rhythmic strength, and the propensity to move,✮✮ en Proceedings of the Sound and Music Computing Conferences, Logos Verlag Berlin, 2013.[72] M. R2022b, ✭✭Spectral flux for audio signals and auditory spectrograms - MATLAB spectralFluxMathWorks Switzerland,✮✮ MathWorks - MATLAB and Simulink Conferences - MATLAB Simulink., direccion: https://ch.mathworks.com/help/audio/ref/spectralflux.html.[73] Essentia, ✭✭Algorithm reference: LogAttackTime — Essentia 2.1-beta6-dev documentation,✮✮ Homepage — Essentia 2.1-beta6-dev documentation., direccion: https://essentia.upf.edu/reference/ ´ streaming LogAttackTime.html.[75] U. internacional de telecomunicaciones, ✭✭Metodos generales para la evaluaci ´ on subjetiva de la ´ calidad de sonido Serie BS Servicio de radiodifusion (sonora), ´ ✮✮ 2019. direccion: http://www.itu. ´ int/publ/R-REC/es.[76] W. Hoeg, L. Christensen y R. Walker, ✭✭Subjective assessment of audio quality-the means and methods in the EBU,✮✮ EBU Technical Review, pags. 40-50, 1997.[77] I. K. Paz, ✭✭Media aritmetica simple, ´ ✮✮ Boletın electronico ´ , vol. 7, pags. 1-13, 2007.[78] J. C. Senar, ✭✭La medicion de la repetibilidad y el error de medida, ´ ✮✮ Etologuıa, vol. 17, pags. 53-64, ´ 1999.[59] V. M. Panaretos e Y. Zemel, ✭✭Statistical aspects of wasserstein distances,✮✮ Annual Review of Statistics and Its Application, vol. 6, 2019, ISSN: 2326831X. DOI: 10.1146/annurev- statistics030718-104938.[74] F. M. H. Arellano y M. B. Mexico, ´ ✭✭El Concepto de Distancia y su Aplicacion en Estadıstica ´ Multivariada,✮✮ Datos Diagnosticos y Tendencias, 1970.qBiblioteca USB Medellín (San Benito): TG-7108t530 - Física::534 - Sonido y vibraciones relacionadasProcesamiento musicalInstrumentos musicalesSonidos sintetizadosMorphing de audioModelamiento sinusoidalSíntesis de audioDescomposición de matrices no negativasAudio morphingSinusoidal modelingAudio synthesisNon-negative matrix decompositionComparacion objetiva y subjetiva de las características sonoras de los audios generados apartir de dos metodos diferentes de síntesis por morphingTrabajo de grado - Pregradohttp://purl.org/coar/resource_type/c_7a1fTextinfo:eu-repo/semantics/bachelorThesishttp://purl.org/redcol/resource_type/TPinfo:eu-repo/semantics/acceptedVersionComunidad Científica y AcadémicaPublicationORIGINALComparacion_Objetiva_Subjetiva_Gutierrez_2023.pdfComparacion_Objetiva_Subjetiva_Gutierrez_2023.pdfapplication/pdf6256960https://bibliotecadigital.usb.edu.co/bitstreams/2752ea98-4e37-4cbf-a5d3-a06f860a17cb/download2ecc47e264ad723334bbebe3702b2ee3MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-82079https://bibliotecadigital.usb.edu.co/bitstreams/febe29a2-6276-492b-a69a-3b1089f20a1a/downloadce8fd7f912f132cbeb263b9ddc893467MD52TEXTComparacion_Objetiva_Subjetiva_Gutierrez_2023.pdf.txtComparacion_Objetiva_Subjetiva_Gutierrez_2023.pdf.txtExtracted texttext/plain101969https://bibliotecadigital.usb.edu.co/bitstreams/9ea51e4f-693a-45c3-875f-a51ccadc14e9/download9f5a7dca5edf5087732d92d7b999b388MD53THUMBNAILComparacion_Objetiva_Subjetiva_Gutierrez_2023.pdf.jpgComparacion_Objetiva_Subjetiva_Gutierrez_2023.pdf.jpgGenerated Thumbnailimage/jpeg6682https://bibliotecadigital.usb.edu.co/bitstreams/3f1bf3e3-fced-4e25-964d-2b1656b46d6b/download16161ff90bce8e4e52789d6b9e352535MD5410819/13463oai:bibliotecadigital.usb.edu.co:10819/134632025-05-02 16:42:38.65https://bibliotecadigital.usb.edu.coRepositorio Institucional Universidad de San Buenaventura Colombiabdigital@metabiblioteca.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 |