Ate noise reduction techniques

Recently, a new speckle noise reduction and contrast enhancement technique has been introduced that is motivated by the research in compressive sampling or sensing. Compressive sampling is based on the principle that a sparse signal such as ultrasound can be fully recovered when sampled below the Nyquist rate. This allows for a new noise reduction technique that preserves the high frequency and fine details while reducing the effects of speckle noise. This method improves the overall perceptual quality of the image for visualization and diagnosis by the radiologist. This paper examines how the improvement in SNR makes the method suitable as a preprocessor to improve a computer aided detection (CAD) system for breast cancer detection. Classical performance metrics such as false positive rates, false negative rates and receiver operator curves will be used to show the benefits of this approach. Initial experiments look promising for microcalcification detection, where the new method yields a false negative rate of 20 percent at a false positive rate of 0.5 percent while the traditional speckle reduction techniques yield a false negative rate of 60 percent at a false positive rate of 0.5 percent.

In this study, the noise reduction properties of the adaptive statistical iterative reconstruction (IR) on two different CT scanners of 64 and 256-slice were compared and their differences were assessed. The homogeneous module of the ACR CT phantom was scanned on the 64 and 256slices CT scanners from the same vendor in the range of 15-40mA. On each scanner, the data were reconstructed using filtered back projection (FBP) and at all strengths of IR with the STANDARD kernel. For each reconstruction, a 3D noise power spectrum (NPS) was calculated and the central frequency ratio in the xy plane (CFRxy ), CFR in the z-direction (CFRz ), and noise magnitude ratio (NMR) were derived. CFR is the central frequency ratio of NPS between the denoised image and the FBP image, and NMR is the ratio of the areas under the NPS curves. Ideally, both CFRxy and CFRz should be near 1, indicating minimal texture changes in both xy and z directions, while NMR should be as close to 0 as possible, indicating more noise reduction. When comparing strengths with equivalent impact on noise texture, IR on the 64-slice reduced the noise magnitude in the xy plane more than that on the 256-slice. In the z-direction, the IR on the 256-slice produced a central frequency shift on the 256-slice but not on the 64-slice. In addition, the noise reduction effects of the IR on the 256-slice were affected when radiation exposure was below 2.0mGy, but there was no observable dose-dependence on the 64-slice. Our noise property analysis revealed that iterative reconstructions on different scanner platforms from the same vendor can be distinct, with unique effects on the noise texture and magnitude in CT images. The IR on a 64-slice scanner provides slightly enhanced noise reduction and maintains a noise reduction rate independent of dose, unlike the one on a 256-slice scanner. Notably, the IR on the 64-slice scanner was a 2D noise reduction technique (NRT), while the one on the 256-slice was a 3D NRT. These observations showcase the impact of different NRTs on clinical CT images, even when comparing the same NRT on different scanners.

Background and Aim: Background noise as a serious challenge mainly affects the speech perception in people with hearing loss. One of the methods used to control noise is digital noise reduction (DNR) technique. The present study aimed to investigate the effect of DNR program (activated and deactivated) on the frequency gain of basic and advanced behind-the-ear (BTE) Oticon hearing aids, using different DNR strategies to reduce background noise. Methods: Two behind-the-ear Oticon hearing aids (Opn1 S105 and GetP) were used in this study. The Affinity 2.0 test box was first used to measure their DNR (off/on) gains using the national acoustic laboratories-non linear2 (NAL-NL2) and desired sensation level multi-stage [input/output] (DSLm[I/O]) formulas at sound pressure levels of 45, 65 and 85 dB SPL at a frequency range of 250-8000 Hz for three hearing loss (HL) patterns using the international speech test signal and broad band noise. Results: There was a significant difference in DNR performance between the Opn1 S105 and GetP models for all three HL patterns at 45 and 65 dB SPL and most frequencies. Conclusion: The DNR performance of advanced and basic hearing aids is different for different HLs at 45 and 65 dB SPL and most frequencies. The performance of advanced hearing aids is significant using the DSLm[I/O] formula at most frequencies. Keywords: Digital noise reduction; international speech test signal; frequency response; broad band noise

Environmental noise and data sparsity present major challenges within the field of bioacoustics. The presence of noise degrades the analysis of audio and field recordings commonly containing large quantities of data with sparse vocalisation features. This work explores noise reduction (audio enhancement) techniques in the context of extremely sparse vocalisations (< 1% occurrence rates) of invasive mammalian and marsupial species, and the clear implications for other bioacoustics applications which face similar challenges. This work compares relevant noise reduction techniques and recommends a spectral subtraction approach. Spectral subtraction achieved a 42.1 dB improvement in signal to noise power (SnNR) and a 2.7 dB improvement in noise variance (SR). We also demonstrate reliable noise reduction at bandwidths up to 250 kHz and efficiency improvements compared to alternative methods. We explore the benefits of deep audio enhancement approaches demonstrating comparable noise reduction with improvements in transient noise reduction but also key limitations such as bandwidth, efficiency and data generation in bioacoustics applications. We identify how the contributions of this work can be applied within the broader context of bioacoustics. All data and code is publicly available at https://github.com/BenMcEwen1/Sparse-Noise-Reduction

Reception threshold for sentences (RTS) as measured by a modified version of the HINT test [Nilsson et al., J. Acoust. Soc. Am. 95, 1085–1099 (1984)] were collected on 26 hearing-impaired listeners fit binaurally with digital hearing aids incorporating a nine-channel spectral subtraction technique of single-microphone noise reduction. Thresholds were measured in quiet and noise presented at a zero degree azimuth with the subjects listening unaided, aided without noise reduction, and aided with noise reduction and no modification to the gain function when noise reduction was activated. Additional data included soundfield thresholds as a frequency-dependent measure of the impact of the noise reduction algorithm on sensitivity to soft sounds. RTS in quiet reveal a significant effect of noise reduction [F(3,69)=46.9, p&amp;lt;0.01] with noise reduction increasing RTS 3.4 dB relative to no noise reduction, from 40.4 dB (A) to 43.8 dB (A). RTS in noise reveal a significant effect of noise reduction [F(3,69)=18.7, p&amp;lt;0.01] with noise reduction decreasing RTS 1.8 dB relative to no noise reduction, from 0.0 dB S/N to 1.8 dB S/N. Soundfield thresholds reveal a loss of sensitivity mainly in frequencies below 1000 Hz. The noise reduction algorithm will be reviewed and discussed.

This paper aims to conduct an acoustic analysis through noise measurements of a hybrid propulsion system intended for implementation on a drone, from which the main noise sources can be identified for further research on noise reduction techniques. Additionally, the noise was characterized by performing spectral analysis and identifying the tonal components that contribute to the overall noise. The propelling force system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The propulsion system consists of a micro-turboshaft coupled with a gearbox connected to an electric generator. The electric current produced by the generator powers an electric ducted fan (EDF). The engineturbo-engine was tested in free-field conditions for noise generation at different speeds, and for this, an array of microphones was installed, positioned polarly around the system and near the intake and exhaust. Consequently, based on the test results, the acoustic directivity was plotted, revealing that the highest noise levels are at the front and rear of the engine. The noise level at a distance of 1.5 m from the turboengine exceeds 90 dBA at all tested speeds. Spectral analyses of both the far-field acoustic signals (measured with a polar microphone array) and the near-field signals (microphones positioned near the intake and exhaust) revealed that the primary contributors to the overall noise are the micromotor’s compressor, specifically the gas dynamic phenomena in the fan (BPF and 2× BPF). Thus, it was determined that at the intake level, the main noise contribution comes from the high-frequency components of the compressor, while at the exhaust level, the noise mainly originates from the combustion chamber, characterized by low-frequency components (up to 2 kHz). The findings from this study have practical applications in the design and development of quieter drone propulsion systems. By identifying and targeting the primary noise sources, engineers can implement effective noise reduction strategies, leading to drones that are less disruptive in urban environments and other noise-sensitive areas. This can enhance the acceptance and deployment of drone technology in various sectors, including logistics, surveillance, and environmental monitoring.

In a move towards standardization of automatic test equipment (ATE), the US Air Force is enforcing utilization of modular ATE (MATE) guides in the development of test systems for both current and future avionics programs. The author discusses the design and production of thirteen automatic depot inertial navigation test systems (ADINTS), using the MATE guides. ADINTS is designed with accessible interfaces, large computer memory, reserved rack space and test table flexibility, all of which make it adaptable to many kinds of units under test. Of particular importance in testing inertial products, a real-time-controlled interface is available. The bus architecture complies with the MIL-STD-1553 A/B requirements. When coupled with the benefits to a depot of standardization, commonality of spare parts, and shorter run times, ADINTS has the potential to provide support to other military services and some commercial users of inertial products both in the USA and overseas, as well as the US Air Force. >

This paper presents system-level design and implementation of an ultra-wide tunable, high precision, fast locking, low phase noise, and low power portable fractional-N frequency synthesizer. The output frequency of the proposed design is ranged from 54 MHz to 6.8GHz. The VCO cores cover frequencies from 3.4GHz to 6.8GHz. The programmable output dividers allow generation of the lower frequencies. The frequency resolution of the implemented system is ±20 parts per million (ppm) over -40/85oC. The output power is tunable between -4dBm and +5dBm. The implemented system has a phase adjust feature that allows shifting of the output phase in relation to the reference oscillator ranged from 0° to 180°. It can generate a wide range, high precision, and linear frequency sweep. The sweep rate, frequency step, and frequency range are tunable. The new frequency tuning algorithm, named Yas algorithm, is proposed to improve frequency precision of the synthesizer. To demonstrate the efficiency of the Yas algorithm, the simulation result MATLAB and the experimental measurements are presented. The system consumes 300mA; therefore, it can be powered by Li-Ion battery. The output phase noise is -95.55 dBc/Hz at 1KHz offset from 3GHz. The experimental measurement results demonstrate that the implemented frequency synthesizer can be used for applications, such as oscillator of spectrum analyzer, automatic test equipment, FMCW radars, high-performance clock source for high speed data converter

We assess the effectiveness of a previously proposed noise reduction technology for hyperspectral imagery to examine whether it can better serve remote sensing applications after noise reduction using the technology. Target detection from hyperspectral imagery using a spectral unmixing approach is selected as an example in the assessment. A hyperspectral datacube acquired using an airborne short-wave-infrared Full Spectrum Image II with man-made targets in the scene of the datacube is tested. Three criteria are proposed and used to evaluate the detectability of the targets derived from the datacube before and after noise reduction. The evaluation results show that the detectability of the targets is significantly improved after noise reduction using the technology. The targets not detected from the original datacube are detected with high confidence after noise reduction using the technology. A noise reduction technique that is based on a smoothing approach is also evaluated for the sake of comparison to the proposed noise reduction technology. It also improves the detectability of the targets, but is less effective than the proposed noise reduction technology.

This paper addresses the problem of single microphone speech enhancement in noisy environments. State of the art short-time noise reduction techniques are most often expressed as a spectral gain depending on the signal-to-noise ratio (SNR). The well-known decision-directed approach drastically limits the level of musical noise but the estimated a priori SNR is biased since it depends on the speech spectrum estimated in the previous frame. The consequence of this bias is an annoying reverberation effect. We propose a new method, called reliable features selection noise reduction (RFSNR) technique, that is able to classify the a posteriori SNR estimates into two categories: the reliable features leading to speech components and the unreliable ones corresponding to musical noise only. Then it is possible to directly enhance speech using these reliable components leading to an unbiased estimator.

We describe the use of a graphical mathematical spreadsheet programming environment which can be used to simulate the acquisition and reconstruction processes of an x-ray computed tomography (CT) machine. The simulation is used to study the effect of photon counting statistics on the noise in the reconstructed image. Finally we describe and evaluate a novel technique for noise reduction using a nonlinear wavelet filter in which the filter thresholds are calculated individually from the "measured" projection data. This filter is shown to compare favorably to threshold filters based on global estimates of noise variance.

This paper proposes a two stage hybrid speech enhancement system with nonuniform subbands. Frequency bins after Fourier transform are nonuniformly grouped to reduce the computations in calculating the spectral gain. First stage includes a soft decision gain modification and applied to the Ephraim-Malah gain function based on minimum mean square error estimation (MMSE) and a psychoacoustic masking threshold is used in the second stage noise reduction. The performance of this algorithm is compared to modified spectral subtraction and spectral weighting algorithms in terms of listening tests and spectrograms. The proposed algorithm is suitable for noise reduction in non-stationary noise environments.

The family of state space-based speech enhancement algorithms is taken as the central building block for this thesis; such algorithms include for example Kalman Filters running on autoregressive models of speech. The goal of this thesis is to render this family of algorithms ready, that is, upgrade the existing solutions to make them more flexible and more robust to real-world situations where different types of nonstationary, non-Gaussian noise are to be expected. The chosen context is that of sensitive applications such as hearing aids for which naturalness, intelligibility, and noise reduction are equally important. Most of the state space-based speech enhancement algorithms are currently unable to operate robustly in nonstationary colored-noise environments, and those found in the literature usually assume AWGN conditions. Since in addition, they are in general more computationally demanding than other well-established algorithms, it is not surprising that they are often either overlooked or omitted in respected and recent speech enhancement publications, as the introduction in this thesis will underline. Nevertheless, for AWGN conditions they have still been reported to yield high quality and natural-sounding results, making them an appealing choice for the range of applications chosen. To achieve the goal of this thesis, it is first necessary to categorize, implement and test many state-space based algorithms for speech enhancement; as it turns out, most of the existing algorithms revolve around the same principles and ideas. Then, in this work several tools and extensions of state spaced-based algorithms are developed in order to tackle the required handling of real-world noise. In parallel, since one of the main goals is to preserve naturalness and intelligibility, a variety of configurations based on these proposed tools and extensions are concocted and thoroughly tested so as to be able to determine the best solution both in terms of output quality and complexity requirements. As an additional constraint, most of the tools devised are meant to be compatible with any state space-based algorithm, rendering the work in this thesis homogeneous and unifying. To deal with real-world noise, novel fullband and subband solutions that take into account information from existing noise estimation algorithms are proposed. In the fullband domain, new all-pole modelling techniques are devised, and in the subband domain several alternatives to handle the noise are proposed as well. Throughout the thesis, several important accessory contributions are also developed, such as a new category of particle filtering algorithms for nonlinear speech models, low-cost post-processing techniques for further noise reduction, binaural extensions of monaural state-space models, and various improvement ideas specifically targeted to state-space algorithms running on autoregressive speech models. As an example, a possible state-space based algorithm is proposed based on some of the conclusions of the several Chapters of this thesis, which is shown to outperform three well-established state-of-the-art algorithms in adverse conditions. Rather than merely justifying the work in this thesis by a final arm-wrestling contest, these latter results prove that state-space based algorithms deserve a place amongst highly regarded and viable algorithms for speech enhancement.

A class of SVD-based signal enhancement procedures is described, which amount to a specific optimal filtering technique for the case where the so-called 'desired response' signal cannot be observed. It is shown that this optimal filter can be written as a function of the generalized singular vectors and singular values of a so-called speech and noise data matrix. A number of simple symmetry properties of the optimal filter are derived, which are valid for the white noise case as well as for the coloured noise case. Also the averaging step of the standard one-microphone SVD-based noise reduction techniques is investigated, leading to serious doubts about the necessity of this averaging step. When applying this technique for multi-microphone noise reduction, it is shown that for simple scenarios, where we consider localised sources and no multipath propagation, this technique exhibits some kind of beamforming behaviour. We further compare the performance of this technique with standard beamforming techniques, showing that for all reverberation times the performance of the SVD-based optimal filter is better than beamforming.

The presence of acoustic noise in audio recordings is an ongoing issue that plagues many applications. This ambient background noise is difficult to reduce due to its unpredictable nature. Many single channel noise reduction techniques exist but are limited in that they may distort the desired speech signal due to overlapping spectral content of the speech and noise. It is therefore of interest to investigate the use of multichannel noise reduction algorithms to further attenuate noise while attempting to preserve the speech signal of interest. Specifically, this thesis looks to investigate the use of microphone arrays in conjunction with multichannel noise reduction algorithms to aid aiding in speaker identification. Recording a speaker in the presence of acoustic background noise ultimately limits the performance and confidence of speaker identification algorithms. In situations where it is impossible to control the noise environment where the speech sample is taken, noise reduction algorithms must be developed and applied to clean the speech signal in order to give speaker identification software a chance at a positive identification. Due to the limitations of single channel techniques, it is of interest to see if spatial information provided by microphone arrays can be exploited to aid in speaker identification. This thesis provides an exploration of several time domain multichannel noise reduction techniques including delay sum beamforming, multi-channel Wiener filtering, and Spatial-Temporal Prediction filtering. Each algorithm is prototyped and filter performance is evaluated using various simulations and experiments. A three-dimensional noise model is developed to simulate and compare the performance of the above methods and experimental results of three data collections are presented and analyzed. The algorithms are compared and recommendations are given for the use of each technique. Finally, ideas for future work are discussed to improve performance and implementation of these multichannel algorithms. Possible applications for this technology include audio surveillance, identity verification, video chatting, conference calling and sound source localization.