Literatura académica sobre el tema "Noise subtraction"
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Artículos de revistas sobre el tema "Noise subtraction"
Ma, Ying, Xiao Hua Zhang y Bing Lei Xing. "A Speech Enhancement Algorithm Based on the “Music Noise” Analysis". Applied Mechanics and Materials 543-547 (marzo de 2014): 2784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2784.
Texto completoLin, Tingting, Xiaokang Yao, Sijia Yu y Yang Zhang. "Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain". Electronics 9, n.º 8 (5 de agosto de 2020): 1254. http://dx.doi.org/10.3390/electronics9081254.
Texto completoLu, Dong Yu, Guan Yu Tian, Xiao Shan Lu, Xin Ma y Lan Tian. "Improved Speech Enhancement Algorithm Based on Bark Bands Noise-Estimation for Non-Stationary Environment". Applied Mechanics and Materials 385-386 (agosto de 2013): 1398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1398.
Texto completoMuzammel, Chowdhury Shahriar, Mahmudul Hasan, Khalil Ahammad y Mousumi Hasan Mukti. "Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique". European Journal of Engineering Research and Science 3, n.º 7 (31 de julio de 2018): 78. http://dx.doi.org/10.24018/ejers.2018.3.7.838.
Texto completoMuzammel, Chowdhury Shahriar, Mahmudul Hasan, Khalil Ahammad y Mousumi Hasan Mukti. "Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique". European Journal of Engineering and Technology Research 3, n.º 7 (31 de julio de 2018): 78–80. http://dx.doi.org/10.24018/ejeng.2018.3.7.838.
Texto completoKawamura, Arata, Weerawut Thanhikam y Youji Iiguni. "Single Channel Speech Enhancement Techniques in Spectral Domain". ISRN Mechanical Engineering 2012 (22 de julio de 2012): 1–9. http://dx.doi.org/10.5402/2012/919234.
Texto completoZhang, Shenghuan y Ye Cheng. "Masking and noise reduction processing of music signals in reverberant music". Journal of Intelligent Systems 31, n.º 1 (1 de enero de 2022): 420–27. http://dx.doi.org/10.1515/jisys-2022-0024.
Texto completoCella, G. "Thermal noise correlations and subtraction". Physics Letters A 382, n.º 33 (agosto de 2018): 2269–74. http://dx.doi.org/10.1016/j.physleta.2017.06.026.
Texto completoDouarche, F., L. Buisson, S. Ciliberto y A. Petrosyan. "A simple noise subtraction technique". Review of Scientific Instruments 75, n.º 12 (diciembre de 2004): 5084–89. http://dx.doi.org/10.1063/1.1821625.
Texto completoAlimi, Isiaka Ajewale. "Performance Improvement of Digital Hearing Aid Systems". Journal of Communications Technology, Electronics and Computer Science 1 (22 de octubre de 2015): 27. http://dx.doi.org/10.22385/jctecs.v1i0.15.
Texto completoTesis sobre el tema "Noise subtraction"
Dandu, Sai Venkata Satya Siva Kumar y Sujit Kadimisetti. "2D SPECTRAL SUBTRACTION FOR NOISE SUPPRESSION IN FINGERPRINT IMAGES". Thesis, Blekinge Tekniska Högskola, Institutionen för tillämpad signalbehandling, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-13848.
Texto completoNolazco, Flores Juan Arturo. "Spectral subtraction and model adaptation for robust speech recognition in noise". Thesis, University of Cambridge, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.318436.
Texto completoFaneuff, Jeffery J. "Spatial, Spectral, and Perceptual Nonlinear Noise Reduction for Hands-free Microphones in a Car". Digital WPI, 2002. https://digitalcommons.wpi.edu/etd-theses/926.
Texto completoBADARACCO, FRANCESCA. "Newtonian Noise studies in 2nd and 3rd generation gravitational-wave interferometric detectors". Doctoral thesis, Gran Sasso Science Institute, 2021. http://hdl.handle.net/20.500.12571/16065.
Texto completoMalavolta, Luca. "Data reduction, radial velocities and stellar parameters from spectra in the very low signal-to-noise domain". Doctoral thesis, Università degli studi di Padova, 2013. http://hdl.handle.net/11577/3423130.
Texto completoTelescopi di grandi dimensioni usualmente rendono disponibili dei programmi per la riduzione dati che restituiscono all’astronomo dati già pronti per l’analisi scientifica, e sempre più spesso gli astronomi si appoggiano a questi programmi per evitare un lavoro lungo e diffi- cile. I programmi di riduzione dati standard sono però progettati per restituire buoni risultati su dati con buon Rapporto Segnale Rumore (RSR), e spesso i problemi legati alla riduzione di dati a basso RSR non sono presi in considerazione, con il risultato che le informazioni che contengono non sono adeguatamente utilizzate. Negli ultimi anni il nostro gruppo di ricerca ha collezionato migli- aia di spettri osservati con lo strumento GIRAFFE collegato al Very Large Telescope dell’Osservatorio Europeo del Sud in Cile, con lo scopo di determinare la distanza geometrica e lo stato dinamico di diversi Ammassi Globulari Galattici, ma in definitiva l’analisi è stata ostaco- lata da errori sistematici nella riduzione e calibrazione dei dati e nella misura delle velocità radiali. Inoltre questi dati non sono mai stati uti- lizzati per determinare altre informazioni come temperatura e metal- licità delle stesse, poiché considerati troppo rumorosi per questo tipo di analisi. In questa tesi concentriamo la nostra attenzione sulla riduzione dati ed analisi di spettri con bassissimo RSR. Il set di dati che analizziamo in questa tesi è composto da 7250 spettri per 2771 stelle dell’ammasso globulare M 4 (NGC 6121) ottenute con GIRAFFE nell’intervallo spet- trale 5145 − 5360Å. Stelle della parte superiore del Ramo delle Giganti Rossi fino alla Sequenza Principale sono state osservate in condizioni molto differenti, comprese notti con luna piena, fino ad raggiungere un valore limite di RSR ≃ 10 per molti spettri. La nostra analisi incomincia con i passi di base della riduzione dati ed estrazione degli spettri, adattando tecniche ben testate in altri campi (come la fotometria) ma ancora non ben sviluppate in spettroscopia. Continuiamo con il migliorare la soluzione della dispersione in lunghezza d’onda la correzione per piccoli spostamenti nelle velocità radiali di riferimento tra i dati di calibrazione presi durante il giorno e le osservazioni scientifiche seguendo un approccio completamente differente rispetto a quello ESO. Analizziamo poi la miglior maniera per effettuare la sottrazione del cielo e la normalizzazione del continuo, le due più importanti fonti rispettivamente di rumore ed errori sistematici nella misura delle velocità radiali nell’analisi chimica degli spettri. L’alto numero di spettri del nostro dataset richiede un approccio automatico ma robusto, che non manchiamo di fornire. Determiniamo infine per il nostro campione di stelle velocità radiali con una precisione mai vista in precedenza per dati di questo tipo e ritroviamo gli stessi parametri atmosferici di altri lavori svolti su stelle più brillanti, con dati a risoluzione spettrale maggiore e su intervalli di lunghezza d’onda dieci volte superiori a quello dei nostri dati. Nell’ultimo capitolo della tesi affrontiamo una problematica simile ma da una prospettiva completamente differente. Spettri ad alta risoluzione e ad alto RSR ottenuti con lo spettrografo HARPS sono stati usati per calibrare i parametri atmosferici stellari in funzione delle caratteristiche di funzioni di cross-correlazione specificatamente costruite includendo linee spettrali con diversa sensibilità ai parametri atmosferici stellari. Questi strumenti sono stati progettati per essere facilmente implementati un programma di riduzione dati, pur tuttavia senza sacrificare l’accuratezza dei parametri determinati anche per spettri a basso Rapporto Segnale Rumore.
Silva, Leandro Aureliano da. "Filtros de Kalman no tempo e freqüência discretos combinados com subtração espectral". Universidade de São Paulo, 2007. http://www.teses.usp.br/teses/disponiveis/18/18152/tde-28082007-104533/.
Texto completoThis work has as main objective to present and to compare techniques of noise reduction using as evaluation criterion the low spectral distortion and the noise reduction in the reconstruction of corrupted speech signals. For so much, it was used the Kalman\'s filters in the time and frequency domain together with the technique of power spectral subtraction. The used signals were corrupted by white and colored noises and the evaluation of effectiveness of the algorithms was accomplished using the segmental signal-to-noise ratio (SNRseg) and the Itakura-Saito distance (d(a,b)). After the processing, it was noticed that the Kalman filtering in the time together with power spectral subtraction presented better results than the Kalman filtering in the frequency together with power spectral subtraction.
Sun, Yu. "SIGNAL PROCESSING FOR SHORT WAVE INFRARED (SWIR) RAMAN SPECTROSCOPY DIAGNOSIS OF CANCER". Master's thesis, Temple University Libraries, 2017. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/446864.
Texto completoM.S.
Raman spectroscopy is an effective optical analysis of the biochemically specific characterization of tissues without contrast agents or exogenous dyes. Applications of Raman spectroscopy include analysis and biomarker investigation, disease diagnosis and surgical guidance. One major challenge in Raman spectroscopy is removing inherent fluorescence background present in samples to acquire Raman signatures. In some tissues, like liver, kidney and darkly pigment skin, the auto-fluorescence background is strong enough to overwhelm the Raman peaks in conventional Near-Infrared (NIR) Raman systems. Recent publications have shown that using Raman systems with excitation sources with wavelengths beyond 830 nm and short-wave infrared (SWIR) InGaAs Array detectors resulted in dramatically reduced auto-fluorescence. The unique characteristics of Raman signals collected from SWIR systems versus NIR Raman systems requires inspection of the suitability of spectral pre-processing techniques. This thesis focused on the development of spectral processing techniques at three different steps; 1) detector background & noise reduction; 2) Auto-fluorescence background subtraction; 3) detection of outlier measurements to assist statistical classification. Detector background and noise reduction was compared between two different techniques, and a direct subtraction method resulted in better performance to reduce fixed pattern noise unique to InGaAs arrays. For the aim 2, three different algorithms for fluorescence background removal were developed, and a modified polynomial fitting method was found to be most appropriate for the low signal-to-noise (SNR) spectra. Finally, local outlier factor(LOF), a multivariate statistical outlier metric, was implemented in a two-stage fashion, and shown to be effective at identifying raw measurement errors and Raman spectra outliers. The overall outcome of this thesis was the evaluation of spectral processing techniques for SWIR Raman spectroscopy systems, and the development of specific techniques to optimize data quality and best prepare spectra for statistical analysis.
Temple University--Theses
Kanda, Allan Zukeran. "Estudo e implementação de uma técnica de redução de ruído em sinais de voz baseada na subtração espectral e em critérios psicoacústicos /". Ilha Solteira : [s.n.], 2010. http://hdl.handle.net/11449/99085.
Texto completoBanca: Suely Cunha Amaro Mantovani
Banca: Marco Aparecido Queiroz Duarte
Resumo: A proposta deste trabalho é aprimorar a performance da técnica de redução de ruído, subtração espectral baseado na relação SNR a Priori, através da implementação de dois novos parâmetros Potência de Articulação e Não-Articulação obtidas a partir de algumas técnicas psicoacústicas. Faz-se um estudo da anatomia do sistema de audição humana e algumas limitações físicas, com o objetivo de entender o princípio básico da técnica ANIQUE, que é um sistema de avaliação objetiva de voz e têm como princípio o modelamento da percepção humana da voz. Através do modelo ANIQUE são extraídas as principais técnicas psicoacústicas para obtenção dos novos parâmetros, Potência de Articulação e Não- Articulação. Procurou-se apresentar de maneira resumida o processo de equacionamento das técnicas de redução de ruído em sinais de voz e das técnicas psicoacústicas. Posteriormente são descritos todos os processos das técnicas utilizadas que foram simuladas utilizando a linguagem de programação do MatLab®, seguido das avaliações objetivas dos sinais processados pelo software PESQ, que é um programa de avaliação objetiva de voz. Os resultados mostram que a implementação das técnicas psicoacústicas foram eficazes para melhorar a performance da técnica subtração espectral baseada na relação SNR a Priori
Abstract: The purpose of this work is to enhance the performance of noise reduction techniques based on spectral subtraction, which take in account the a priori signal-to-noise (SNR a Priori) to be estimated considering psychoacoustic criteria. in order to understand the basic principle of the ANIQUE, which is a psychoacoustic based technique used to evaluate the quality of speech signals, it was necessary to develop a study of the anatomy of the human hearing and some physical limitations, From the ANIQUE are extracted new parameters namely Articulation and Non-Articulation Powers, used to estimate the SNR_prio. As a result, it was obtained a new spectral based technique which was implemented in the MatLab® environment and evaluated using the objective quality measure for speech signal simulations namely PESQ. The results show that the implementation of psychoacoustic techniques were effective in enhance the performance of the spectral subtraction technique based on SNR a Priori
Mestre
Lamoš, Martin. "Modelování metod číslicového zpracování obrazu u digitální radiografie". Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2010. http://www.nusl.cz/ntk/nusl-218768.
Texto completoSingh, Latchman. "Speech enhancement for forensic applications". Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36080/1/36080_Singh_1998.pdf.
Texto completoLibros sobre el tema "Noise subtraction"
Baral, Suman. Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30904-6.
Texto completoBaral, Suman. Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD. Springer, 2019.
Buscar texto completoBaral, Suman. Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD. Springer, 2020.
Buscar texto completoCapítulos de libros sobre el tema "Noise subtraction"
Vaseghi, Saeed V. "Spectral Subtraction". En Advanced Signal Processing and Digital Noise Reduction, 242–60. Wiesbaden: Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-92773-6_9.
Texto completoCella, G. "Off-Line Subtraction of Seismic Newtonian Noise". En Recent Developments in General Relativity, 495–503. Milano: Springer Milan, 2000. http://dx.doi.org/10.1007/978-88-470-2113-6_44.
Texto completoAbuzneid, Abdelshakour, Moeen Uddin, Shaid Ali Naz y Omar Abuzaghleh. "An Algorithm to Remove Noise from Audio Signal by Noise Subtraction". En Advances in Computer and Information Sciences and Engineering, 5–10. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8741-7_2.
Texto completoBaral, Suman. "New Noise Subtraction Methods for Lattice QCD Calculations". En Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD, 1–32. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30904-6_1.
Texto completoStarke, Ludger, Thoralf Niendorf y Sonia Waiczies. "Data Preparation Protocol for Low Signal-to-Noise Ratio Fluorine-19 MRI". En Methods in Molecular Biology, 711–22. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_43.
Texto completoKatsuda, T., T. Gotanda, R. Gotanda, T. Akagawa, N. Tanki, T. Kuwano y K. Yabunaka. "Noise reduction of radiochromic film: median filter processing of subtraction image". En IFMBE Proceedings, 763–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_188.
Texto completoUdrea, Radu Mihnea, Claudia Cristina Oprea y Cristian Stanciu. "Multi-microphone Noise Reduction System Integrating Nonlinear Multi-band Spectral Subtraction". En Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 133–38. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74935-8_19.
Texto completoCzyżewski, Andrzej. "Intelligent Control of Spectral Subtraction Algorithm for Noise Removal from Audio". En Intelligent Tools for Building a Scientific Information Platform, 475–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-35647-6_28.
Texto completoRam, Rashmirekha, Saumendra Kumar Mohapatra y Mihir Narayan Mohanty. "Speech Enhancement Using a Novel Spectral Subtraction Method for Seashore Noise". En Lecture Notes in Electrical Engineering, 217–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8752-8_23.
Texto completoBaral, Suman. "TF Model for Mesonic Matters". En Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD, 33–72. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30904-6_2.
Texto completoActas de conferencias sobre el tema "Noise subtraction"
Poletto, F. y A. Craglietto. "Orthogonalized noise subtraction". En 53rd EAEG Meeting. European Association of Geoscientists & Engineers, 1991. http://dx.doi.org/10.3997/2214-4609.201411022.
Texto completoSpitz, S. "Pattern Recognition and Subtraction of Coherent Noise". En 60th EAGE Conference and Exhibition. European Association of Geoscientists & Engineers, 1998. http://dx.doi.org/10.3997/2214-4609.201408108.
Texto completoLinhard, Klaus y Tim Haulick. "Spectral noise subtraction with recursive gain curves". En 5th International Conference on Spoken Language Processing (ICSLP 1998). ISCA: ISCA, 1998. http://dx.doi.org/10.21437/icslp.1998-333.
Texto completoLinhard, Klaus y Tim Haulick. "Noise subtraction with parametric recursive gain curves". En 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA: ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-575.
Texto completoLathoud, G., M. Magimai-Doss, B. Mesot y H. Bourlard. "Unsupervised spectral subtraction for noise-robust ASR". En IEEE Workshop on Automatic Speech Recognition and Understanding, 2005. IEEE, 2005. http://dx.doi.org/10.1109/asru.2005.1566500.
Texto completoBaral, Suman, Walter Wilcox y Ronald B. Morgan. "New Noise Subtraction Methods in Lattice QCD". En 34th annual International Symposium on Lattice Field Theory. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.256.0355.
Texto completoGuerrero, Victor X., Walter Wilcox y Ronald B. Morgan. "Eigenspectrum noise subtraction methods in lattice QCD". En The XXVII International Symposium on Lattice Field Theory. Trieste, Italy: Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.091.0041.
Texto completoCole, Cliston, Marc Karam y Heshmat Aglan. "Spectral Subtraction of Noise in Speech Processing Applications". En 2008 40th Southeastern Symposium on System Theory (SSST). IEEE, 2008. http://dx.doi.org/10.1109/ssst.2008.4480188.
Texto completoMoeller, R. P. y W. K. Burns. "1.06 µm All-fiber Gyroscope With Noise Subtraction". En Optical Fiber Sensors. Washington, D.C.: OSA, 1992. http://dx.doi.org/10.1364/ofs.1992.p2.
Texto completoIzquierdo, M. A. G., M. G. Hernández, M. Molero y J. J. Anaya. "Structural noise reduction using multiresolution-based spectral subtraction". En International Congress on Ultrasonics. Vienna University of Technology, 2007. http://dx.doi.org/10.3728/icultrasonics.2007.vienna.1290_izquierdo.
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