Showing papers on "Noise measurement published in 2013"

The Diverse Environments Multi-channel Acoustic Noise Database (DEMAND): A database of multichannel environmental noise recordings

Abstract: Multi-microphone arrays allow for the use of spatial filtering techniques that can greatly improve noise reduction and source separation. However, for speech and audio data, work on noise reduction or separation has focused primarily on one- or two-channel systems. Because of this, databases of multichannel environmental noise are not widely available. DEMAND (Diverse Environments Multi-channel Acoustic Noise Database) addresses this problem by providing a set of 16-channel noise files recorded in a variety of indoor and outdoor settings. The data was recorded using a planar microphone array consisting of four staggered rows, with the smallest distance between microphones being 5 cm and the largest being 21.8 cm. DEMAND is freely available under a Creative Commons license to encourage research into algorithms beyond the stereo setup.

Single-Image Noise Level Estimation for Blind Denoising

Abstract: Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.

Image Denoising Using the Higher Order Singular Value Decomposition

Abstract: In this paper, we propose a very simple and elegant patch-based, machine learning technique for image denoising using the higher order singular value decomposition (HOSVD). The technique simply groups together similar patches from a noisy image (with similarity defined by a statistically motivated criterion) into a 3D stack, computes the HOSVD coefficients of this stack, manipulates these coefficients by hard thresholding, and inverts the HOSVD transform to produce the final filtered image. Our technique chooses all required parameters in a principled way, relating them to the noise model. We also discuss our motivation for adopting the HOSVD as an appropriate transform for image denoising. We experimentally demonstrate the excellent performance of the technique on grayscale as well as color images. On color images, our method produces state-of-the-art results, outperforming other color image denoising algorithms at moderately high noise levels. A criterion for optimal patch-size selection and noise variance estimation from the residual images (after denoising) is also presented.

Apparatus and method to classify sound to detect speech

Abstract: Audio frames are classified as either speech, non-transient background noise, or transient noise events. Probabilities of speech or transient noise event, or other metrics may be calculated to indicate confidence in classification. Frames classified as speech or noise events are not used in updating models (e.g., spectral subtraction noise estimates, silence model, background energy estimates, signal-to-noise ratio) of non-transient background noise. Frame classification affects acceptance/rejection of recognition hypothesis. Classifications and other audio related information may be determined by circuitry in a headset, and sent (e.g., wirelessly) to a separate processor-based recognition device.

Impulsive Noise Mitigation in Powerline Communications Using Sparse Bayesian Learning

Abstract: Asynchronous impulsive noise and periodic impulsive noises limit communication performance in OFDM powerline communication systems. Conventional OFDM receivers that assume additive white Gaussian noise experience degradation in communication performance in impulsive noise. Alternate designs assume a statistical noise model and use the model parameters in mitigating impulsive noise. These receivers require training overhead for parameter estimation, and degrade due to model and parameter mismatch. To mitigate asynchronous impulsive noise, we exploit its sparsity in the time domain, and apply sparse Bayesian learning methods to estimate and subtract the noise impulses. We propose three iterative algorithms with different complexity vs. performance trade-offs: (1) we utilize the noise projection onto null and pilot tones; (2) we add the information in the date tones to perform joint noise estimation and symbol detection; (3) we use decision feedback from the decoder to further enhance the accuracy of noise estimation. These algorithms are also embedded in a time-domain block interleaving OFDM system to mitigate periodic impulsive noise. Compared to conventional OFDM receivers, the proposed methods achieve SNR gains of up to 9 dB in coded and 10 dB in uncoded systems in asynchronous impulsive noise, and up to 6 dB in coded systems in periodic impulsive noise.

Efficient High-Dimensional Inference in the Multiple Measurement Vector Problem

Abstract: In this work, a Bayesian approximate message passing algorithm is proposed for solving the multiple measurement vector (MMV) problem in compressive sensing, in which a collection of sparse signal vectors that share a common support are recovered from undersampled noisy measurements. The algorithm, AMP-MMV, is capable of exploiting temporal correlations in the amplitudes of non-zero coefficients, and provides soft estimates of the signal vectors as well as the underlying support. Central to the proposed approach is an extension of recently developed approximate message passing techniques to the amplitude-correlated MMV setting. Aided by these techniques, AMP-MMV offers a computational complexity that is linear in all problem dimensions. In order to allow for automatic parameter tuning, an expectation-maximization algorithm that complements AMP-MMV is described. Finally, a detailed numerical study demonstrates the power of the proposed approach and its particular suitability for application to high-dimensional problems.

On the Fundamental Limits of Adaptive Sensing

Abstract: Suppose we can sequentially acquire arbitrary linear measurements of an n -dimensional vector x resulting in the linear model y = A x + z, where z represents measurement noise. If the signal is known to be sparse, one would expect the following folk theorem to be true: choosing an adaptive strategy which cleverly selects the next row of A based on what has been previously observed should do far better than a nonadaptive strategy which sets the rows of A ahead of time, thus not trying to learn anything about the signal in between observations. This paper shows that the folk theorem is false. We prove that the advantages offered by clever adaptive strategies and sophisticated estimation procedures-no matter how intractable-over classical compressed acquisition/recovery schemes are, in general, minimal.

Additive White Gaussian Noise Level Estimation in SVD Domain for Images

Abstract: Accurate estimation of Gaussian noise level is of fundamental interest in a wide variety of vision and image processing applications as it is critical to the processing techniques that follow. In this paper, a new effective noise level estimation method is proposed on the basis of the study of singular values of noise-corrupted images. Two novel aspects of this paper address the major challenges in noise estimation: 1) the use of the tail of singular values for noise estimation to alleviate the influence of the signal on the data basis for the noise estimation process and 2) the addition of known noise to estimate the content-dependent parameter, so that the proposed scheme is adaptive to visual signals, thereby enabling a wider application scope of the proposed scheme. The analysis and experiment results demonstrate that the proposed algorithm can reliably infer noise levels and show robust behavior over a wide range of visual content and noise conditions, and that is outperforms relevant existing methods.

A Comparative Study of Various Types of Image Noise and Efficient Noise Removal Techniques

Abstract: Getting an efficient method of removing noise from the images, before processing them for further analysis is a great challenge for the researchers. Noise can degrade the image at the time of capturing or transmission of the image. Before applying image processing tools to an image, noise removal from the images is done at highest priority. Ample algorithms are available, but they have their own assumptions, merits and demerits. The kind of the noise removal algorithms to remove the noise depends on the type of noise present in the image. Best results are obtained if testing image model follows the assumptions and fail otherwise. In this paper, light is thrown on some important type of noise and a comparative analysis of noise removal techniques is done. This paper presents the results of applying different noise types to an image model and investigates the results of applying various noise reduction techniques.

A Weighted Dictionary Learning Model for Denoising Images Corrupted by Mixed Noise

Abstract: This paper proposes a general weighted l2-l0 norms energy minimization model to remove mixed noise such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise from the images. The approach is built upon maximum likelihood estimation framework and sparse representations over a trained dictionary. Rather than optimizing the likelihood functional derived from a mixture distribution, we present a new weighting data fidelity function, which has the same minimizer as the original likelihood functional but is much easier to optimize. The weighting function in the model can be determined by the algorithm itself, and it plays a role of noise detection in terms of the different estimated noise parameters. By incorporating the sparse regularization of small image patches, the proposed method can efficiently remove a variety of mixed or single noise while preserving the image textures well. In addition, a modified K-SVD algorithm is designed to address the weighted rank-one approximation. The experimental results demonstrate its better performance compared with some existing methods.

A Markov-Middleton Model for Bursty Impulsive Noise: Modeling and Receiver Design

Abstract: Transmission over channels impaired by impulsive noise, such as in power substations, calls for peculiar mitigation techniques at the receiver side in order to cope with signal deterioration. For these techniques to be effective, a reliable noise model is usually required. One of the widely accepted models is the Middleton Class A, which presents the twofold advantage to be canonical (i.e., invariant of the particular physical source mechanisms) and to exhibit a simple probability density function (PDF) that only depends on three physical parameters, making this model very attractive. However, such a model fails in replicating bursty impulsive noise, where each impulse spans over several consecutive noise samples, as usually observed (e.g., in power substations). Indeed, the Middleton Class A model only deals with amplitude or envelope statistics. On the other hand, for models based on Markov chains, although they reproduce the bursty nature of impulses, the determination of the suitable number of states and the noise distribution associated with each state can be challenging. In this paper, 1) we introduce a new impulsive noise model which is, in fact, a Hidden Markov Model, whose realizations exactly follow a Middleton Class A distribution and 2) we evaluate optimum and suboptimum detections for a coded transmission impaired by the proposed noise model.

Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography

Abstract: Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.

An Efficient Denoising Architecture for Removal of Impulse Noise in Images

Abstract: Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose an efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise. To achieve the goal of low cost, a low-complexity VLSI architecture is proposed. We employ a decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Our extensive experimental results demonstrate that the proposed technique can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods. Moreover, the performance can be comparable to the higher,- complexity methods. The VLSI architecture of our design yields a processing rate of about 200 MHz by using TSMC 0.18 μm technology. Compared with the state-of-the-art techniques, this work can reduce memory storage by more than 99 percent. The design requires only low computational complexity and two line memory buffers. Its hardware cost is low and suitable to be applied to many real-time applications.

Spectral probability density as a tool for ambient noise analysis.

Abstract: This paper presents the empirical probability density of the power spectral density as a tool to assess the field performance of passive acoustic monitoring systems and the statistical distribution of underwater noise levels across the frequency spectrum. Using example datasets, it is shown that this method can reveal limitations such as persistent tonal components and insufficient dynamic range, which may be undetected by conventional techniques. The method is then combined with spectral averages and percentiles, which illustrates how the underlying noise level distributions influence these metrics. This combined approach is proposed as a standard, integrative presentation of ambient noise spectra.

Frequency and direction-dependent ambient sound handling in personal audio devices having adaptive noise cancellation (anc)

Abstract: A personal audio device, such as a wireless telephone, includes noise canceling circuit that adaptively generates an anti-noise signal from a reference microphone signal and injects the anti-noise signal into the speaker or other transducer output to cause cancellation of ambient audio sounds. An error microphone may also be provided proximate the speaker to measure the output of the transducer in order to control the adaptation of the anti-noise signal and to estimate an electro-acoustical path from the noise canceling circuit through the transducer. A processing circuit that performs the adaptive noise canceling (ANC) function also detects frequency-dependent characteristics in and/or direction of the ambient sounds and alters adaptation of the noise canceling circuit in response to the detection.

Time-of-Arrival Estimation Based on Information Theoretic Criteria

Abstract: The possibility to accurately localize tags by using wireless techniques is of great importance for several emerging applications in the Internet of Things. Precise ranging can be obtained with ultra wideband (UWB) impulse radio (IR) systems, where short impulses are transmitted, and their time-of-arrival (ToA) is estimated at the receiver. Due to the presence of noise and multipath, the estimator has the difficult task of discriminating the time intervals where the received waveform is due to noise only, by those where there are also signal components. Common low-complexity methods use an energy detector (ED), whose output is compared with a threshold, to discriminate the time intervals containing noise only from those containing signal plus noise. Optimal threshold design for these methods requires knowledge of the channel impulse response and of the receiver noise power. We propose a different approach, where ToA estimation is based on model selection by information theoretic criteria (ITC). The resulting ToA algorithms do not use thresholds, and do not require any information about the channel or the noise power level. These blind, universal ToA estimators show, for completely unknown multipath channels and in the presence of noise with unknown power, excellent performance when compared with ideal genie-aided schemes.

Noise burst adaptation of secondary path adaptive response in noise-canceling personal audio devices

Abstract: A personal audio device, such as a wireless telephone, generates an anti-noise signal from an error microphone signal and injects the anti-noise signal into the speaker or other transducer output to cause cancellation of ambient audio sounds. The error microphone is also provided proximate the speaker to provide an error signal indicative of the effectiveness of the noise cancellation. A secondary path estimating adaptive filter is used to estimate the electro-acoustical path from the noise canceling circuit through the transducer so that source audio can be removed from the error signal. Noise bursts are injected intermittently and the adaptation of the secondary path estimating adaptive filter controlled, so that the secondary path estimate can be maintained irrespective of the presence and amplitude of the source audio.

Application of a recently introduced index for acoustic complexity to an avian soundscape with traffic noise.

Abstract: An altered acoustic environment can have severe consequences for natural communities, especially for species that use acoustic signals to communicate and achieve breeding success. Numerous studies have focused on traffic noise disturbance, but the possible causes of road effects are inter-correlated and the literature on noise qua noise is sometimes contradictory. To provide further empirical data in this regard, the authors investigated the spatio-temporal variability of the singing dynamics of an avian community living in an acoustic context altered by traffic noise. Fieldwork was carried out in a wood of Turkey oaks (central Italy) bordered on one side by a main road. The soundscape was examined by positioning eight digital recorders, distributed in two transects perpendicular to the road, and recording between 6:30 and 8.30 a.m. for 12 continuous sessions. The acoustic complexity index was used to obtain a quantification of singing dynamics, which were positively correlated with traffic noise. This may indicate that birds try to propagate their signals with greater emphasis (e.g., amplified redundancy or loudness of the songs) to override the masking effect of noise. Nevertheless, an ecotonal effect could have influenced the correlation results, with this enhanced dynamic possibly being due to a more densely populated environment.

SNR Estimation for Multi-dimensional Cognitive Receiver under Correlated Channel/Noise

Abstract: In addition to Spectrum Sensing (SS) capability required by a Cognitive Radio (CR), Signal to Noise Ratio (SNR) estimation of the primary signals at the CR receiver is crucial in order to adapt its coverage area dynamically using underlay techniques. In practical scenarios, channel and noise may be correlated due to various reasons and SNR estimation techniques with the assumption of white noise and uncorrelated channel may not be suitable for estimating the primary SNR. In this paper, firstly, we study the performance of different eigenvalue-based SS techniques in the presence of channel or/and noise correlation. Secondly, we carry out detailed theoretical analysis of the signal plus noise hypothesis to derive the asymptotic eigenvalue probability distribution function (a.e.p.d.f.) of the received signal's covariance matrix under the following two cases: (i) correlated channel and white noise, and (ii) correlated channel and correlated noise, which is the main contribution of this paper. Finally, an SNR estimation technique based on the derived a.e.p.d.f is proposed in the presence of channel/noise correlation and its performance is evaluated in terms of normalized Mean Square Error (MSE). It is shown that the PU SNR can be reliably estimated when the CR sensing module is aware of the channel/noise correlation.

Kalman-Filter-Based Approach for Multisensor, Multitrack, and Multitemporal InSAR

Abstract: A Kalman-filter-based approach is presented for resolving 3-D surface displacements using multisensor, multitrack, and multitemporal interferometric synthetic aperture radar (SAR) measurements. Measurements from each interferogram are projected into the three reference directions and combined in the Kalman filter model with displacements determined from previous interferograms to produce updated displacement measurements. Both simulated and real data sets are used to test the proposed approach. It is found that the method works well when the measurement noise is low. The displacements in the north direction, however, are much lower in accuracy than those in the other two directions and even become unstable when the measurement noise is high due to the polar-orbiting imaging geometries of the current satellite SAR sensors.

Noise Measurements of Discrete HEMT Transistors and Application to Wideband Very Low-Noise Amplifiers

Abstract: The noise models of InP and GaAs HEMTs are compared with measurements at both 300 and 20 K The critical parameter, Tdrain, in the Pospieszalski noise model is determined as a function of drain current by measurements of the 1-GHz noise of discrete transistors with 50- Ω generator impedance The dc I-V for the transistors under test are presented and effects of impact-ionization are noted InP devices with both 100% and 75% indium mole fraction in channel are included Examples of the design and measurement of very wideband low-noise amplifiers (LNAs) using the tested transistors are presented At 20-K physical temperature the GaAs LNA achieves 10-K noise over the 07-16-GHz range with 16 mW of power and an InP LNA measures 20-K noise over the 6-50-GHz range with 30 mW of power

Measurement Bounds for Sparse Signal Ensembles via Graphical Models

Abstract: In compressive sensing, a small collection of linear projections of a sparse signal contains enough information to permit signal recovery. Distributed compressive sensing extends this framework by defining ensemble sparsity models, allowing a correlated ensemble of sparse signals to be jointly recovered from a collection of separately acquired compressive measurements. In this paper, we introduce a framework for modeling sparse signal ensembles that quantifies the intra- and intersignal dependences within and among the signals. This framework is based on a novel bipartite graph representation that links the sparse signal coefficients with the measurements obtained for each signal. Using our framework, we provide fundamental bounds on the number of noiseless measurements that each sensor must collect to ensure that the signals are jointly recoverable.

Three-Dimensional Application of Damas Methodology for Aeroacoustic Noise Source Definition

Abstract: At the 2004 AIAA/CEAS Aeroacoustic Conference, a breakthrough in acoustic microphone array technology was reported by the authors. A Deconvolution Approach for the Mapping of Acoustic Sources (DAMAS) was developed which decouples the array design and processing influence from the noise being measured, using a simple and robust algorithm. For several prior airframe noise studies, it was shown to permit an unambiguous and accurate determination of acoustic source position and strength. As a follow-on effort, this paper examines the technique for three-dimensional (3D) applications. First, the beamforming ability for arrays, of different size and design, to focus longitudinally and laterally is examined for a range of source positions and frequency. Advantage is found for larger array designs with higher density microphone distributions towards the center. After defining a 3D grid generalized with respect to the array s beamforming characteristics, DAMAS is employed in simulated and experimental noise test cases. It is found that spatial resolution is much less sharp in the longitudinal direction in front of the array compared to side-to-side lateral resolution. 3D DAMAS becomes useful for sufficiently large arrays at sufficiently high frequency. But, such can be a challenge to computational capabilities, with regard to the required expanse and number of grid points. Also, larger arrays can strain basic physical modeling assumptions that DAMAS and all traditional array methodologies use. An important experimental result is that turbulent shear layers can negatively impact attainable beamforming resolution. Still, the usefulness of 3D DAMAS is demonstrated by the measurement of landing gear noise source distributions in a difficult hard-wall wind tunnel environment.

Image informative maps for component- wise estimating parameters of signal- dependent noise

Abstract: We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramer-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

Online estimation of noise parameters for Kalman filter

Abstract: A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented.