Готові списки джерел за темами / Noise subtraction

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Добірка наукової літератури з теми "Noise subtraction"

Автор: Grafiati
Опубліковано: 11 березня 2023

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Статті в журналах з теми "Noise subtraction"

1

Ma, Ying, Xiao Hua Zhang, and Bing Lei Xing. "A Speech Enhancement Algorithm Based on the “Music Noise” Analysis." Applied Mechanics and Materials 543-547 (March 2014): 2784–87. http://dx.doi.org/10.4028/www.scientific.net/amm.543-547.2784.

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Анотація:
Interference is inevitable process of voice communication will be from the surrounding environment and transmission medium noise, communication equipment, electronic noise, and other speakers. These interference makes the voice receiver received for noisy speech signal with noise pollution. According to the traditional spectral subtraction residual musical noise is too strong, the weighted processing is reduced and the power spectrum correction, spectral subtraction method was adopted to improve the traditional. According to the analysis of real speech data collection simulation, improved spectral subtraction can effectively reduce the musical noise, can satisfy the requirement of speech enhancement.
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2

Lin, Tingting, Xiaokang Yao, Sijia Yu, and Yang Zhang. "Electromagnetic Noise Suppression of Magnetic Resonance Sounding Combined with Data Acquisition and Multi-Frame Spectral Subtraction in the Frequency Domain." Electronics 9, no. 8 (August 5, 2020): 1254. http://dx.doi.org/10.3390/electronics9081254.

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Анотація:
As an advanced groundwater detection method, magnetic resonance sounding (MRS) has received more and more attention. However, the biggest challenge is that MRS measurements always suffer with a bad signal-to-noise ratio (SNR). Aiming at the problem of noise interference in MRS measurement, we propose a novel noise-suppression approach based on the combination of data acquisition and multi-frame spectral subtraction (DA-MFSS). The pure ambient noise from the measurement area is first collected by the receiving coil, and then the noisy MRS signal is recorded following the pulse moments transmitting. The procedure of the pure noise and the noisy MRS signal acquisition will be repeated several times. Then, the pure noise and the noisy signal are averaged to preliminarily suppress the noise. Secondly, the averaged pure noise and the noisy signal are divided into multiple frames. The framed signal is transformed into the frequency domain and the spectral subtraction method is applied to further suppress the electromagnetic noise embedded in the noisy MRS signal. Finally, the de-noised signal is recovered by the overlap-add method and inverse Fourier transformation. The approach was examined by numerical simulation and field measurements. After applying the proposed approach, the SNR of the MRS data was improved by 16.89 dB and both the random noise and the harmonic noise were well suppressed.
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3

Lu, Dong Yu, Guan Yu Tian, Xiao Shan Lu, Xin Ma, and Lan Tian. "Improved Speech Enhancement Algorithm Based on Bark Bands Noise-Estimation for Non-Stationary Environment." Applied Mechanics and Materials 385-386 (August 2013): 1398–401. http://dx.doi.org/10.4028/www.scientific.net/amm.385-386.1398.

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Анотація:
The conventional spectrum subtraction algorithm cannot effectively suppress the noise under highly non-stationary environment and results in the remaining music noise is often heard in the enhanced speech. In order to improve the speech enhancement performance, a novel denoising algorithm is proposed, which is based on speech endpoint detection using spectrum variance and the dynamic spectrum subtraction in Bark bands. According to human auditory characteristics, the Bark bands spectrums of the noisy speech signal are firstly calculated, and the noise power spectrum of each Bark band is then tracked and estimated by the improved minima controlled recursive averaging method. This noise estimation is adjustable frame by frame and more accurate for non-stationary environment. The experiment results showed that the proposed method can suppress the noise more efficiently than the conventional spectrum subtraction and the remaining music noise is almost eliminated.
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4

Muzammel, Chowdhury Shahriar, Mahmudul Hasan, Khalil Ahammad, and Mousumi Hasan Mukti. "Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique." European Journal of Engineering Research and Science 3, no. 7 (July 31, 2018): 78. http://dx.doi.org/10.24018/ejers.2018.3.7.838.

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Анотація:
Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.
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5

Muzammel, Chowdhury Shahriar, Mahmudul Hasan, Khalil Ahammad, and Mousumi Hasan Mukti. "Noise Reduction from Speech Signals using Modified Spectral Subtraction Technique." European Journal of Engineering and Technology Research 3, no. 7 (July 31, 2018): 78–80. http://dx.doi.org/10.24018/ejeng.2018.3.7.838.

Повний текст джерела
Анотація:
Varieties of environmental sources of noise and distortion can degrade the quality of the speech signal in a communication system. This research work explores the effects of these interfering sounds on speech applications and introduces a technique for reducing their influence and enhancing the acceptability and intelligibility of the speech signal. In this work, a noise reduction system using single microphone method in time domain to improve SNR of noise contaminated speech is proposed. Traditional Spectral Subtraction method has been reviewed very well and the relationship with wiener filter is also illustrated. The Spectral Subtraction method has been generalized and the focus is put on reducing noise from speech in single channel signals. Voice Activity Detector (VAD) is ignored in this proposed system, because a-priori information about the noise is assumed. The research has been conducted using Gaussian White Noise and Color Noise. The experimental result shows a remarkable improvement in SNR for the generalized version and it is noticed that the result is very much satisfactory when white noises are added but the addition of color noise produces a comparatively poor improvement report. The system has been tested with eight different datasets and on an average, 65.27% improvement in SNR (Signal to Noise Ratio) for White Noise using Generalized Spectral Subtraction Method is achieved comparing with Traditional Spectral Subtraction Method. The average improvement in SNR for Color Noise recorded is 53.31%. The Generalized Spectral Subtraction method is shown to improve the speech quality and to improve SNR as well.
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6

Kawamura, Arata, Weerawut Thanhikam, and Youji Iiguni. "Single Channel Speech Enhancement Techniques in Spectral Domain." ISRN Mechanical Engineering 2012 (July 22, 2012): 1–9. http://dx.doi.org/10.5402/2012/919234.

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Анотація:
This paper presents single-channel speech enhancement techniques in spectral domain. One of the most famous single channel speech enhancement techniques is the spectral subtraction method proposed by S.F. Boll in 1979. In this method, an estimated speech spectrum is obtained by simply subtracting a preestimated noise spectrum from an observed one. Hence, the spectral subtraction method is not concerned with speech spectral properties. It is well known that the spectral subtraction method produces an annoying artificial noise in the extracted speech signal. On the other hand, recent successful speech enhancement methods positively utilize the speech property and achieve an efficient speech enhancement capability. This paper presents a historical review about some speech estimation techniques and explicitly states the difference between their theoretical back-ground. Moreover, to evaluate their speech enhancement capabilities, we perform computer simulations. The results show that an adaptive speech enhancement method based on MAP estimation gives the best noise reduction capability in comparison to other speech enhancement methods presented in this paper.
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7

Zhang, Shenghuan, and Ye Cheng. "Masking and noise reduction processing of music signals in reverberant music." Journal of Intelligent Systems 31, no. 1 (January 1, 2022): 420–27. http://dx.doi.org/10.1515/jisys-2022-0024.

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Анотація:
Abstract Noise will be inevitably mixed with music signals in the recording process. To improve the quality of music signals, it is necessary to reduce noise as much as possible. This article briefly introduces noise, the masking effect, and the spectral subtraction method for reducing noise in reverberant music. The spectral subtraction method was improved by the human ear masking effect to enhance its noise reduction performance. Simulation experiments were carried out on the traditional and improved spectral subtraction methods. The results showed that the improved spectral subtraction method could reduce the noise in reverberant music more effectively; under an objective evaluation criterion, the signal-to-noise ratio, the de-reverberated music signal processed by the improved spectral subtraction method had a higher signal-to-noise ratio; under a subjective evaluation criterion, mean opinion score (MOS), the de-reverberated music signal processed by the improved spectral subtraction method also had a better evaluation.
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8

Cella, G. "Thermal noise correlations and subtraction." Physics Letters A 382, no. 33 (August 2018): 2269–74. http://dx.doi.org/10.1016/j.physleta.2017.06.026.

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9

Douarche, F., L. Buisson, S. Ciliberto, and A. Petrosyan. "A simple noise subtraction technique." Review of Scientific Instruments 75, no. 12 (December 2004): 5084–89. http://dx.doi.org/10.1063/1.1821625.

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10

Alimi, Isiaka Ajewale. "Performance Improvement of Digital Hearing Aid Systems." Journal of Communications Technology, Electronics and Computer Science 1 (October 22, 2015): 27. http://dx.doi.org/10.22385/jctecs.v1i0.15.

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Анотація:
Digital hearing aids addresses the issues of noise and speech intelligibility that is associated with the analogue types. One of the main functions of the digital signal processor (DSP) of digital hearing aid systems is noise reduction which can be achieved by speech enhancement algorithms which in turn improve system performance and flexibility. However, studies have shown that the quality of experience (QoE) with some of the current hearing aids is not up to expectation in a noisy environment due to interfering sound, background noise and reverberation. It is also suggested that noise reduction features of the DSP can be further improved accordingly. Recently, we proposed an adaptive spectral subtraction algorithm to enhance the performance of communication systems and address the issue of associated musical noise generated by the conventional spectral subtraction algorithm. The effectiveness of the algorithm has been confirmed by different objective and subjective evaluations. In this study, an adaptive spectral subtraction algorithm is implemented using the noise-estimation algorithm for highly non-stationary noisy environments instead of the voice activity detection (VAD) employed in our previous work due to its effectiveness. Also, signal to residual spectrum ratio (SR) is implemented in order to control the amplification distortion for speech intelligibility improvement. The results show that the proposed scheme gives comparatively better performance and can be easily employed in digital hearing aid system for improving speech quality and intelligibility.
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Більше джерел

Дисертації з теми "Noise subtraction"

1

Dandu, Sai Venkata Satya Siva Kumar, and 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.

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Анотація:
Human fingerprints are rich in details called the minutiae, which can be used as identification marks for fingerprint verification. To get the details, the fingerprint capturing techniques are to be improved. Since when we the fingerprint is captured, the noise from outside adds to it. The goal of this thesis is to remove the noise present in the fingerprint image. To achieve a good quality fingerprint image, this noise has to be removed or suppressed and here it is done by using an algorithm or technique called ’Spectral Subtraction’, where the algorithm is based on subtraction of estimated noise spectrum from noisy signal spectrum. The performance of the algorithm is assessed by comparing the original fingerprint image and image obtained after spectral subtraction several parameters like PSNR, SSIM and also for different fingerprints on the database. Finally, performance matching was done using NIST matching software, and the obtained results were presented in the form of Receiver Operating Characteristics (ROC)graphs, using MATLAB, and the experimental results were presented.
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2

Nolazco, 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.

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3

Faneuff, 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.

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Анотація:
"Speech enhancement in an automobile is a challenging problem because interference can come from engine noise, fans, music, wind, road noise, reverberation, echo, and passengers engaging in other conversations. Hands-free microphones make the situation worse because the strength of the desired speech signal reduces with increased distance between the microphone and talker. Automobile safety is improved when the driver can use a hands-free interface to phones and other devices instead of taking his eyes off the road. The demand for high quality hands-free communication in the automobile requires the introduction of more powerful algorithms. This thesis shows that a unique combination of five algorithms can achieve superior speech enhancement for a hands-free system when compared to beamforming or spectral subtraction alone. Several different designs were analyzed and tested before converging on the configuration that achieved the best results. Beamforming, voice activity detection, spectral subtraction, perceptual nonlinear weighting, and talker isolation via pitch tracking all work together in a complementary iterative manner to create a speech enhancement system capable of significantly enhancing real world speech signals. The following conclusions are supported by the simulation results using data recorded in a car and are in strong agreement with theory. Adaptive beamforming, like the Generalized Side-lobe Canceller (GSC), can be effectively used if the filters only adapt during silent data frames because too much of the desired speech is cancelled otherwise. Spectral subtraction removes stationary noise while perceptual weighting prevents the introduction of offensive audible noise artifacts. Talker isolation via pitch tracking can perform better when used after beamforming and spectral subtraction because of the higher accuracy obtained after initial noise removal. Iterating the algorithm once increases the accuracy of the Voice Activity Detection (VAD), which improves the overall performance of the algorithm. Placing the microphone(s) on the ceiling above the head and slightly forward of the desired talker appears to be the best location in an automobile based on the experiments performed in this thesis. Objective speech quality measures show that the algorithm removes a majority of the stationary noise in a hands-free environment of an automobile with relatively minimal speech distortion."
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4

BADARACCO, 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.

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Анотація:
This thesis work fits in the Newtonian noise (NN) cancellation framework for gravitational-wave (GW) detectors of 2nd and 3rd generation. At frequencies below 20 Hz the NN affects GW detectors by generating gravity gradients that mask the GW signals that we want to measure. My work can be divided in three main tasks: the optimization of a seismic array for the NN cancellation in underground detectors, the optimization of a seismic array for Advanced Virgo + (which, respect to the former one, relied on seismic measurements and not on a seismic model) and the evaluation of the NN and the seismic field at the KAGRA site. I will briefly summarize in the following the main results of these three works. In the first work I performed a global optimization for finding the optimal locations of an array of sensors for the NN cancellation for underground detectors. Since we need to search for the optimal positions of N sensors in a 3D space, the computational efforts required are very demanding. At the present time, seismic correlations in the relevant frequency band for ET from 3Hz to 20Hz are not available. So we modelled the seismic field as isotropic and homogeneous. With this work I was able to assess the feasibility of applying active NN reduction in underground detectors and reaching a factor 10 of noise reduction with 15 sensors at 10 Hz. In 2019 this work was published. The second work I made during my PhD was conceptually similar to the previous one but very different in the approach used to solve it. Exploiting a theoretical model in Virgo was not an option given its complicated structure. I then used Virgo's seismic data to run the optimization of sensor locations. The main challenge here was that I had to perform a gaussian process regression over a 4D space, and not enough data were available for this purpose. I found a way to bypass the regression over the 4D space by exploiting the convolution theorem. This allowed me to perform the regression over a space with reduced dimension, i.e., in 2D. The global optimization algorithm was then run hundreds of times in order to statistically prove the global minimum, exactly as done in the work for the underground optimization. The results proved that with 15 seismometers we can reach a noise reduction factor of 3-7, which is enough for the aimed sensitivity of the next observing runs. The results of this work were then used to set the array that will be used to cancel the NN in Advanced Virgo +. This work has been published in 2020. This approach could also be useful in future, where it will be needed to optimize underground seismic arrays with real seismic data. Finally, in the third work I used seismic data collected in the Kamioka mine (where the gravitational-wave detector KAGRA is hosted) to investigate the seismic noise caused by the infrastructure and to calculate a NN budget. These are important aspects that need to be investigated in view of the 3rd generation GW detector Einstein Telescope. The data indicated that the infrastructure noise starts to be important well above 10 Hz, where the NN loses its impact on the detector and where the seismic isolation system is capable of killing the noise. Moreover, I used the data from three seismometers to perform a beamforming analysis and find the seismic velocities and the seismic wave main directions. The extracted values were then used as a reference for the estimation of the NN budget. For completeness, I also estimated the NN budget coming from surface Rayleigh waves. This was made by exploiting the data of the F-net network, in Japan. I then showed that the NN from surface and body waves can be neglected for KAGRA.
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5

Malavolta, 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.

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Анотація:
Large astronomical facilities usually provide data reduction pipeline designed to deliver ready-to-use scientific data, and too often as- tronomers are relying on this to avoid the most difficult part of an astronomer job Standard data reduction pipelines however are usu- ally designed and tested to have good performance on data with av- erage Signal to Noise Ratio (SNR) data, and the issues that are related with the reduction of data in the very low SNR domain are not taken int account properly. As a result, informations in data with low SNR are not optimally exploited. During the last decade our group has collected thousands of spec- tra using the GIRAFFE spectrograph at Very Large Telescope (Chile) of the European Southern Observatory (ESO) to determine the ge- ometrical distance and dynamical state of several Galactic Globular Clusters but ultimately the analysis has been hampered by system- atics in data reduction, calibration and radial velocity measurements. Moreover these data has never been exploited to get other informa- tions like temperature and metallicity of stars, because considered too noisy for these kind of analyses. In this thesis we focus our attention on data reduction and analysis of spectra with very low SNR. The dataset we analyze in this thesis comprises 7250 spectra for 2771 stars of the Globular Cluster M 4 (NGC 6121) in the wavelength region 5145 − 5360Å obtained with GIRAFFE. Stars from the upper Red Giant Branch down to the Main Sequence have been observed in very different conditions, including nights close to full moon, and reaching SNR ≃ 10 for many spectra in the dataset. We will first review the basic steps of data reduction and spec- tral extraction, adapting techniques well tested in other field (like photometry) but still under-developed in spectroscopy. We improve the wavelength dispersion solution and the correction of radial veloc- ity shift between day-time calibrations and science observations by following a completely different approach with respect to the ESO pipeline. We then analyze deeply the best way to perform sky sub- traction and continuum normalization, the most important sources respectively of noise and systematics in radial velocity determination and chemical analysis of spectra. The huge number of spectra of our dataset requires an automatic but robust approach, which we do not fail to provide. We finally determine radial velocities for the stars in the sample with unprecedented precision with respect to previous works with similar data and we recover the same stellar atmosphere parameters of other studies performed on the same cluster but on brighter stars, with higher spectral resolution and wavelength range ten times larger than our data. In the final chapter of the thesis we face a similar problem but from a completely different perspective. High resolution, high SNR data from the High Accuracy Radial Velocity Planet Searcher spectro- graph (HARPS) in La Silla (Chile) have been used to calibrate the at- mospheric stellar parameters as functions of the main characteristics of Cross-Correlation Functions, specifically built by including spec- tral lines with different sensitivity to stellar atmosphere parameters. These tools has been designed to be quick and to be easy to imple- ment in a instrument pipeline for a real-time determination, neverthe- less they provide accurate parameters even for lower SNR spectra.
Telescopi 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.
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6

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/.

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Анотація:
Este trabalho tem a finalidade de apresentar e comparar técnicas de redução de ruído utilizando como critérios de avaliação a mínima distorção espectral e a redução de ruído, na reconstrução dos sinais de voz degradados por ruído. Para tanto, utilizou-se os filtros de Kalman de tempo discreto e de freqüência discreta em conjunto com a técnica de subtração espectral de potência. Os sinais utilizados foram contaminados por ruídos branco e colorido, e a avaliação do desempenho dos algoritmos foi realizada tendo-se como parâmetros a relação sinal/ruído segmentada (SNRseg) e a distância de Itakura-Saito (d(a,b)). Após o processamento, verificou-se que a técnica, proposta neste trabalho, de filtragem de Kalman no tempo em conjunto com a subtração espectral de potência, apresentou resultados um pouco melhores em relação à filtragem de Kalman na freqüência em conjunto com a subtração espectral de potência.
This 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.
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7

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.

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Анотація:
Bioengineering
M.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
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8

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.

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Orientador: Jozué Vieira Filho
Banca: 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
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9

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.

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This paper describes a MATLAB application with the main purpose of the simulation of noise components and noise elimination methods of Digital Radiography. The main parts of simulator are the model of a scene, procedures for loading the noise components to image data and methods for image processing. Various methods are employed depending on the type of noise. Subtraction techniques are used for the elimination of structural noise. The physical noise suppression is obtained using several methods of cumulation and Pixel Shift is used to reduce motion artifacts caused by the existence of moving noise. The techniques of superposition highlight the areas of interest in an image. Included are also auxiliary procedures for simulator running and presentation of final data. The model and the presented application can be used mainly for educational purposes as a powerful didactic tool.
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10

Singh, Latchman. "Speech enhancement for forensic applications." Thesis, Queensland University of Technology, 1998. https://eprints.qut.edu.au/36080/1/36080_Singh_1998.pdf.

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Forensic audio recordings are usually made with a single covert microphone in non-ideal conditions. In non-ideal conditions the recordings are highly susceptible to various types of noise. The noise is usually broadband noise, co-talker interference, impulsive noise, narrow band noise or convolutional noise. There are existing speech enhancement techniques available to suppress most of the noise types mentioned, but when the noise is of a considerable level the performance of most enhancement techniques tend to decrease significantly. This thesis presents a study of speech enhancement techniques that are applicable to the enhancement of forensic audio recordings or that can be used in a forensic recording environment. It considers both pre-processing and post-processing speech enhancement techniques. This thesis investigates the improvement of some of the existing speech enhancement techniques as well as proposing some new ones. The performance of the improved and proposed speech enhancement techniques were evaluated objectively using the segmental signal-to-noise ratio (SNRseg) and subjectively using the mean opinion score (MOS). A review of the current speech enhancement techniques is presented in the thesis and is also used as a reference in some comparisons. The current speech enhancement techniques considered are those that are applicable to forensic audio recordings. The performance of the existing techniques are assessed in the comparisons with the speech enhancement techniques proposed by this thesis. Two pre-processing speech enhancement techniques are presented in this thesis. The first pre-processing speech enhancement technique is designed to improve existing broadband noise suppression techniques by the use of frequency shift keying (FSK) signals. It is based on a simple concept, which is to insert a known tone of sufficient amplitude into the silent segments of a speech signal prior to transmission. At the receiver the detection of silent or non-speech segments used in estimating the noise, becomes a simpler and more accurate task due to the inserted tone. The second pre-processing speech enhancement technique is designed to suppress a wide range of noises and it is based on zero padding. Zero padding involves inserting a zero value sample in between each speech signal sample prior to transmission. The inserted zero value samples allow accurate characterisation of the noise in the adjacent speech samples. At the receiver the noise is estimated from the sample positions allocated for the zero value samples. Several post-processing speech enhancement techniques are presented in this thesis. The first post-processing speech enhancement technique is designed for the suppression of co-talker interference and it uses a combination of dynamic time warping (DTW) and dual channel adaptive filtering. This technique is proposed for the suppression of co-talker interference, when the co-talker interference or noise reference signal is obtainable at a later instance as in the case of many covert forensic recordings. The corrupted speech signal and the noise reference signal are aligned using DTW and then the co-talker interference is suppressed using a dual channel adaptive filter. The second post-processing speech enhancement technique is designed for broadband noise suppression and is based on spectral subtraction but it incorporates the masking properties of the human auditory system for improved performance. Auditory masking is used to find the masking threshold, below which the noise is no longer perceivable. Only those noise components above the masking threshold are suppressed. This approach is taken to reduce any byproducts such as musical noise. The third post-processing speech enhancement technique is designed for broadband noise suppression and is based on spectral subtraction but it exploits the human auditory systems perception of frequency. Critical band analysis is used to group frequencies that are similarly perceived, which are then treated as a single entity by the enhancement technique.
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Книги з теми "Noise subtraction"

1

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.

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2

Baral, Suman. Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD. Springer, 2019.

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3

Baral, Suman. Thomas-Fermi Model for Mesons and Noise Subtraction Techniques in Lattice QCD. Springer, 2020.

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Частини книг з теми "Noise subtraction"

1

Vaseghi, Saeed V. "Spectral Subtraction." In 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.

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2

Cella, G. "Off-Line Subtraction of Seismic Newtonian Noise." In Recent Developments in General Relativity, 495–503. Milano: Springer Milan, 2000. http://dx.doi.org/10.1007/978-88-470-2113-6_44.

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3

Abuzneid, Abdelshakour, Moeen Uddin, Shaid Ali Naz, and Omar Abuzaghleh. "An Algorithm to Remove Noise from Audio Signal by Noise Subtraction." In 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.

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4

Baral, Suman. "New Noise Subtraction Methods for Lattice QCD Calculations." In 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.

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5

Starke, Ludger, Thoralf Niendorf, and Sonia Waiczies. "Data Preparation Protocol for Low Signal-to-Noise Ratio Fluorine-19 MRI." In Methods in Molecular Biology, 711–22. New York, NY: Springer US, 2021. http://dx.doi.org/10.1007/978-1-0716-0978-1_43.

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AbstractFluorine-19 MRI shows great promise for a wide range of applications including renal imaging, yet the typically low signal-to-noise ratios and sparse signal distribution necessitate a thorough data preparation.This chapter describes a general data preparation workflow for fluorine MRI experiments. The main processing steps are: (1) estimation of noise level, (2) correction of noise-induced bias and (3) background subtraction. The protocol is supplemented by an example script and toolbox available online.This chapter is based upon work from the COST Action PARENCHIMA, a community-driven network funded by the European Cooperation in Science and Technology (COST) program of the European Union, which aims to improve the reproducibility and standardization of renal MRI biomarkers. This analysis protocol chapter is complemented by two separate chapters describing the basic concept and experimental procedure.
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6

Katsuda, T., T. Gotanda, R. Gotanda, T. Akagawa, N. Tanki, T. Kuwano, and K. Yabunaka. "Noise reduction of radiochromic film: median filter processing of subtraction image." In IFMBE Proceedings, 763–66. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19387-8_188.

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7

Udrea, Radu Mihnea, Claudia Cristina Oprea, and Cristian Stanciu. "Multi-microphone Noise Reduction System Integrating Nonlinear Multi-band Spectral Subtraction." In 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.

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Czyżewski, Andrzej. "Intelligent Control of Spectral Subtraction Algorithm for Noise Removal from Audio." In 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.

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Ram, Rashmirekha, Saumendra Kumar Mohapatra, and Mihir Narayan Mohanty. "Speech Enhancement Using a Novel Spectral Subtraction Method for Seashore Noise." In Lecture Notes in Electrical Engineering, 217–26. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-15-8752-8_23.

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10

Baral, Suman. "TF Model for Mesonic Matters." In 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.

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Тези доповідей конференцій з теми "Noise subtraction"

1

Poletto, F., and A. Craglietto. "Orthogonalized noise subtraction." In 53rd EAEG Meeting. European Association of Geoscientists & Engineers, 1991. http://dx.doi.org/10.3997/2214-4609.201411022.

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2

Spitz, S. "Pattern Recognition and Subtraction of Coherent Noise." In 60th EAGE Conference and Exhibition. European Association of Geoscientists & Engineers, 1998. http://dx.doi.org/10.3997/2214-4609.201408108.

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3

Linhard, Klaus, and Tim Haulick. "Spectral noise subtraction with recursive gain curves." In 5th International Conference on Spoken Language Processing (ICSLP 1998). ISCA: ISCA, 1998. http://dx.doi.org/10.21437/icslp.1998-333.

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4

Linhard, Klaus, and Tim Haulick. "Noise subtraction with parametric recursive gain curves." In 6th European Conference on Speech Communication and Technology (Eurospeech 1999). ISCA: ISCA, 1999. http://dx.doi.org/10.21437/eurospeech.1999-575.

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Lathoud, G., M. Magimai-Doss, B. Mesot, and H. Bourlard. "Unsupervised spectral subtraction for noise-robust ASR." In IEEE Workshop on Automatic Speech Recognition and Understanding, 2005. IEEE, 2005. http://dx.doi.org/10.1109/asru.2005.1566500.

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6

Baral, Suman, Walter Wilcox, and Ronald B. Morgan. "New Noise Subtraction Methods in Lattice QCD." In 34th annual International Symposium on Lattice Field Theory. Trieste, Italy: Sissa Medialab, 2017. http://dx.doi.org/10.22323/1.256.0355.

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Guerrero, Victor X., Walter Wilcox, and Ronald B. Morgan. "Eigenspectrum noise subtraction methods in lattice QCD." In The XXVII International Symposium on Lattice Field Theory. Trieste, Italy: Sissa Medialab, 2010. http://dx.doi.org/10.22323/1.091.0041.

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Cole, Cliston, Marc Karam, and Heshmat Aglan. "Spectral Subtraction of Noise in Speech Processing Applications." In 2008 40th Southeastern Symposium on System Theory (SSST). IEEE, 2008. http://dx.doi.org/10.1109/ssst.2008.4480188.

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Moeller, R. P., and W. K. Burns. "1.06 µm All-fiber Gyroscope With Noise Subtraction." In Optical Fiber Sensors. Washington, D.C.: OSA, 1992. http://dx.doi.org/10.1364/ofs.1992.p2.

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Izquierdo, M. A. G., M. G. Hernández, M. Molero, and J. J. Anaya. "Structural noise reduction using multiresolution-based spectral subtraction." In International Congress on Ultrasonics. Vienna University of Technology, 2007. http://dx.doi.org/10.3728/icultrasonics.2007.vienna.1290_izquierdo.

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