Buildings located near airports may be subjected to significant noise levels due to aircraft flyovers. Aircraft noise is particularly annoying when compared to other traffic noises due to its intermittent nature. While noise control is typically performed at the source, sound insulation programs are in place to improve the acoustic performance of a residence affected by the flyovers. Noise Level Reduction (NLR) is a common metric used in the United States to determine whether a residence qualifies for such programs. Sound insulation programs are available to houses that have an indoor Day Night Average Sound Level (DNL) greater than 45 dBA. NLR is a single-number metric used to quantify the ability for a building or building element to reduce the transmission of external sound pressure levels generated by aircraft. In addition to determining whether a residence qualifies, NLR can be used to quantify the effectiveness of the modifications performed as a result of the sound insulation program. NLR measurements with a loudspeaker offer an alternative method to those performed with aircraft flyovers, offering flexibility to the consultants that perform these measurements in the field. The purpose of this research was to better understand and improve the loudspeaker test for measuring NLR, providing a resource to the aircraft noise industry. Testing was completed on a "test house" that was constructed on campus with construction methods typical of a mixed-humid climate. The angular dependency, repeatability, and reproducibility of NLR, among other factors, were evaluated with field measurements. Significant NLR variations were observed with changes in lateral and vertical angles of incidence.

Current aircraft noise guidelines are based primarily on outdoor sound levels. However, human perception is highly related to indoor response, particularly for residences. A research project has been conducted that provides insight into how typical residential dwelling envelopes affect sound transmitted indoors. A focus has been placed on the effect of residential dwelling envelopes on subsonic civil aircraft noise. Typical construction types across the United States have been identified and used to develop model predictions of outdoor-to-indoor transmission loss. While it was initially hypothesized that these construction types could be grouped by climate region, it was found that these constructions are better grouped according to their outermost construction layer. Further, the impact of systematically altering construction variables (such as the construction materials used and the ratio of window area to wall area) has been investigated. Results will be used to better understand trends for expected noise reduction for typical construction types around the United States. Additionally, comparisons have been made between the effect of older and more modern wall construction techniques on whole-house performance.

Noise is a circumstance of ordinary life and mainly originated by continually growing dynamic and the rapid development of society. In the industrial environment there are noise-intensive parts that influence the condition of present operators negatively. Thus, measures of noise abatement have been investigated intensely by industrial companies as well as federal agencies. As a subdomain of fluid power pneumatics is especially known for characteristic noise emission by the use of air as power transmission. The transient decompression of air from pressure levels up to 8 bar and partly high flow rates nearby sonic speed cause the emergence of direct airborne noise at vent ports of pneumatic components. The following paper outlines the mechanisms that induce the emission of high sound pressure levels. In order to achieve reproducible results a test bench for varying pneumatic standard components is introduced. Based on a selection of those components results are compared to standardized measurement procedures whether fulfilling the standardizations’ requirements. In conclusion, two benefits are achieved. Firstly, standardization is derived enabling neutral comparison of standard pneumatic components’ noise emission. Secondly, the measures can be evaluated to determine the most promising way to redesign pneumatic components of lower noise emissions.

Electromagnetically driven dosing pumps are suitable for metering any kind of liquid in motor vehicles in a precise manner. Due to the working principle and the pump design, an undesired noise occurs when the armature reaches the mechanical end stops. The noise can be reduced by an adequate self-learning control of the supply energy using a position estimation and velocity control. Based on preliminary investigations /1/, a method for noise reduction is realised by using a user-friendly, tiny and cost-efficient hardware, which enables a use in series manufacturing. The method requires only a voltage and current measurement as input signals. The core of the hardware is an 8-bit microcontroller with 8 kilobytes flash memory including necessary peripherals. A smart software development enables an implementation of the entire noise reduction method onto the tiny flash memory.

Enhancing low light videos has been quite a challenge over the years. A video taken in low light always has the issues of low dynamic range and high noise. This master thesis presents contribution within the field of low light video enhancement. Three models are proposed with different tone mapping algorithms for extremely low light low quality video enhancement. For temporal noise removal, a motion compensated kalman structure is presented. Dynamic range of the low light video is stretched using three different methods. In Model 1, dynamic range is increased by adjustment of RGB histograms using gamma correction with a modified version of adaptive clipping thresholds. In Model 2, a shape preserving dynamic range stretch of the RGB histogram is applied using SMQT. In Model 3, contrast enhancement is done using CLAHE. In the final stage, the residual noise is removed using an efficient NLM. The performance of the models are compared on various Objective VQA metrics like NIQE, GCF and SSIM. To evaluate the actual performance of the models subjective tests are conducted, due to the large number of applications that target humans as the end user of the video.The performance of the three models are compared for a total of ten real time input videos taken in extremely low light environment. A total of 25 human observers subjectively evaluated the performance of the three models based on the parameters: contrast, visibility, visually pleasing, amount of noise and overall quality. A detailed statistical evaluation of the relative performance of the three models is also provided.

[pt] Non-local means é um novo algoritmo de redução de ruídos para imagens apresentado por Buades e Morel em 2004. Este algoritmo funciona consideravelmente melhor do que os algoritmos anteriores, mas sua lenta execução causada pela alta complexidade o impede de ser usado em aplicações comuns. O objetivo deste trabalho é investigar maneiras de reduzir o tempo de execução do algoritmo, possibilitando seu uso em aplicações comuns de processamento de imagem, tal como fotografia e centros de impressão. / [en] Non Local Means is an innovative noise reduction algorithm for images presented by Buades and Morel in 2004. It performs remarkably better than older generation algorithms but has a performance penalty that prevents it from being used in mainstream consumer application. The objective of this work is to find ways of reducing the time-complexity of the algorithm and enabling its use in main stream image processing applications such as home photography or photo printing centers.

Resumo: Neste trabalho é feito um estudo sobre os métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método de redução de ruído em sinais de voz no domínio wavelet. O princípio básico da maioria dos métodos de redução de ruído baseados em wavelets é a determinação e aplicação de um limiar, que permite bons resultados para sinais contaminados por ruído branco, mas não são eficientes no processamento de sinais contaminados por ruído colorido, que é o tipo de ruído mais comum em situações reais. Nesses métodos, o limiar, geralmente, é calculado nos intervalos de silêncio e aplicado em todo o sinal. Os coeficientes no domínio wavelet são comparados com este limiar e aqueles que estão abaixo deste valor são eliminados, fazendo assim uma aplicação linear deste limiar. Esta eliminação acaba causando descontinuidades no tempo e na freqüência no sinal processado. Além disso, a forma com que o limiar é calculado pode degradar os trechos de voz do sinal processado, principalmente nos casos em que o limiar depende fortemente da última janela do último trecho de silêncio. O método proposto neste trabalho também é baseado em corte por limiar, mas em vez de uma aplicação linear do limiar, ele faz uma aplicação não-linear, o que evita as descontinuidades causadas por outros algoritmos. O limiar é calculado nos trechos de silêncio e não depende apenas da última janela do último trecho de silêncio, mas sim de todas as janelas, já que este limiar é uma média de todos os limiares calculados neste trecho. Isto faz com que a redução do ruído seja mais uniforme e introduza menos distorções no sinal processado. Além disso, nos trechos de voz ainda é calculado um novo limiar que também será usado, em conjunto com o limiar calculado no silêncio. Isto faz com que a energia da janela que... (Resumo completo, clicar acesso eletrônico abaixo). / Abstract: In this work a study of additive noise reduction in speech based on wavelets is presented and, based on this study a new noise reduction method in speech in the wavelet domain is proposed. The basic idea of most methods of noise reduction based on wavelets is the determination and application of a threshold, that produces good results for signals contaminated by white noise, but they are not very efficient in processing signals contaminated by colored noise, which is more common in real situations. In those methods, the threshold, generally, is calculated in the silence intervals and applied to the whole signal. The coefficients in the wavelet domain are compared with this threshold and those that are below this value are eliminated, making a linear application of this threshold. This elimination causes discontinuities in time and frequency of the processed signal. Besides, the way that the threshold is computed can degrade the voice segments of the processed signal, principally when the threshold depends strongly on the last window of the last silence segment. The proposed method in this work is also based in thresholding, but, instead of a linear application of the threshold, it makes a non-linear application, which avoids the discontinuities caused by other algorithms. The threshold is calculated in the silence segments and is not dependent only on the last window of the last silence segment, but of all the windows, since this threshold is an average of all thresholds calculated in this segment. It makes noise reduction more uniform and introduces less distortion in the processed signal. Besides, in the voice segments a new threshold is calculated that will be also used with the threshold calculated in the silence. It makes that the energy of the window that is being processed is also considered. This way, it is... (Complete abstract, click electronic address below). / Orientador: Francisco Villarreal Alvarado / Coorientador: Jozué Vieira Filho / Banca: Carlos Roberto Minussi / Banca: Fernando Oscar Runstein / Banca: Roberto Kawakami Harrop Galvão / Banca: Ricardo Tokio Higuti / Doutor

Resumo: Neste trabalho é feito um estudo dos métodos de redução de ruído aditivo em sinais de voz baseados em wavelets e, através deste estudo, propõe-se um novo método não-limiar para redução de ruído em sinais de voz no domínio wavelet. Em geral os sinais de voz podem estar contaminados com ruídos artificiais ou reais. O problema consiste que dado um sinal limpo adiciona-se o ruído branco ou colorido, obtendo assim o sinal ruidoso, ambos no domínio do tempo. O que se propõe neste trabalho, é aplicar a transformada wavelet, obtendo assim o sinal transformado no domínio wavelet, reduzindo ou atenuando o ruído sem o uso de limiar. Os métodos mais usados no domínio wavelet são os métodos de redução por limiar, pois permitem bons resultados para sinais contaminados por ruído branco, mas não são eficientes no processamento de sinais contaminados por ruído colorido, que é o tipo de ruído mais comum em situações reais. Nesses métodos, o limiar, geralmente, é calculado nos intervalos de silêncio e aplicado em todo o sinal. Os coeficientes no domínio wavelet são comparados com este limiar e aqueles que estão abaixo deste valor são eliminados ou reduzidos, fazendo assim uma aplicação linear deste limiar. Esta eliminação, na maioria das vezes, causa descontinuidades no tempo e na frequência no sinal processado. Além disso, a forma com que o limiar é calculado pode degradar os trechos de voz do sinal processado, principalmente nos casos em que o limiar depende fortemente da última janela do último trecho de silêncio. O método proposto nesta pesquisa consiste na execução de três processamentos, agindo de acordo com as suas características nas regiões de voz e silêncio, sem o uso de limiar. A execução dos três processamentos é sintetizada numa única função, denominada de função de transferência, que atua como um filtro no processamento do sinal... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: In this work a study of the methods for speech noise reduction based on wavelets is done and, through this study, a new non-thresholding method for speech noise reduction in the wavelet domain is proposed. Generally, a speech signal may be corrupted by artificial or real noise. Let a clean signal be corrupted by white or colored noise, rising a noisy signal in time domain. This work proposes the wavelet application to which gives rise to in the wavelet domain. In this domain, noise is reduced or attenuated without a threshold use. After, the signal is recomposed using the inverse discrete wavelet transform. The most used methods in the wavelet domain wavelet are the thresholding reduction methods, because they allow good results for signals corrupted by white noise, but they do not have the same efficiency when processing signals corrupted by colored noise, this is the most common noise in real situations. In those methods, the threshold is usually calculated in the silence intervals and applied to the whole signal. The coefficients in the wavelet domain are compared with this threshold and those that have absolute value below this value are eliminated or reduced, making a linear application of this threshold. This elimination causes discontinuities in time and in the frequency of the processed signal. Besides, the form with that the threshold is applied can degrade the voice segments of the processed signal, principally in cases that the threshold depends strongly on the last window of the last silence segment. The method proposed in this research consists in the execution of three processing, acting according to their characteristics in the voice and silence segments, without the threshold use. The three processing execution is synthesized in an unique function, called transfer function, acting as a filter in the signal processing. This method has as main objective the overcoming... (Complete abstract click electronic access below) / Orientador: Francisco Villarreal Alvarado / Coorientador: Jozué Vieira Filho / Banca: Carlos Roberto Minussi / Banca: Ailton Akira Shinoda / Banca: Jorge Diaz Calle / Banca: Leandro de Campos Teixeira Gomes / Doutor

"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."

<p>Denna uppsats syftar till att undersöka användbarheten av metoden Independent Component Analysis (ICA) för brusreducering av bilder tagna av infraröda kameror. Speciellt fokus ligger på att reducera additivt brus. Bruset delas upp i två delar, det Gaussiska bruset samt det sensorspecifika mönsterbruset. För att reducera det Gaussiska bruset används en populär metod kallad sparse code shrinkage som bygger på ICA. En ny metod, även den byggandes på ICA, utvecklas för att reducera mönsterbrus. För varje sensor utförs, i den nya metoden, en analys av bilddata för att manuellt identifiera typiska mönsterbruskomponenter. Dessa komponenter används därefter för att reducera mönsterbruset i bilder tagna av den aktuella sensorn. Det visas att metoderna ger goda resultat på infraröda bilder. Algoritmerna testas både på syntetiska såväl som på verkliga bilder och resultat presenteras och jämförs med andra algoritmer.</p> / <p>The purpose of this thesis is to evaluate the applicability of the method Independent Component Analysis (ICA) for noise reduction of infrared images. The focus lies on reducing the additive uncorrelated noise and the sensor specific additive Fixed Pattern Noise (FPN). The well known method sparse code shrinkage, in combination with ICA, is applied to reduce the uncorrelated noise degrading infrared images. The result is compared to an adaptive Wiener filter. A novel method, also based on ICA, for reducing FPN is developed. An independent component analysis is made on images from an infrared sensor and typical fixed pattern noise components are manually identified. The identified components are used to fast and effectively reduce the FPN in images taken by the specific sensor. It is shown that both the FPN reduction algorithm and the sparse code shrinkage method work well for infrared images. The algorithms are tested on synthetic as well as on real images and the performance is measured.</p>