Showing papers on "Background noise published in 2014"

First-Photon Imaging

Abstract: Imagers that use their own illumination can capture three-dimensional (3D) structure and reflectivity information. With photon-counting detectors, images can be acquired at extremely low photon fluxes. To suppress the Poisson noise inherent in low-flux operation, such imagers typically require hundreds of detected photons per pixel for accurate range and reflectivity determination. We introduce a low-flux imaging technique, called first-photon imaging, which is a computational imager that exploits spatial correlations found in real-world scenes and the physics of low-flux measurements. Our technique recovers 3D structure and reflectivity from the first detected photon at each pixel. We demonstrate simultaneous acquisition of sub-pulse duration range and 4-bit reflectivity information in the presence of high background noise. First-photon imaging may be of considerable value to both microscopy and remote sensing.

Optimized Backhaul Compression for Uplink Cloud Radio Access Network

Abstract: This paper studies the uplink of a cloud radio access network (C-RAN) where the cell sites are connected to a cloud-computing-based central processor (CP) with noiseless backhaul links with finite capacities. We employ a simple compress-and-forward scheme in which the base stations (BSs) quantize the received signals and send the quantized signals to the CP using either distributed Wyner-Ziv coding or single-user compression. The CP first decodes the quantization codewords and then decodes the user messages as if the remote users and the cloud center form a virtual multiple-access channel (VMAC). This paper formulates the problem of optimizing the quantization noise levels for weighted sum rate maximization under a sum backhaul capacity constraint. We propose an alternating convex optimization approach to find a local optimum solution to the problem efficiently, and more importantly, to establish that setting the quantization noise levels to be proportional to the background noise levels is near optimal for sum-rate maximization when the signal-to-quantization-noise-ratio (SQNR) is high. In addition, with Wyner-Ziv coding, the approximate quantization noise level is shown to achieve the sum-capacity of the uplink C-RAN model to within a constant gap. With single-user compression, a similar constant-gap result is obtained under a diagonal dominant channel condition. These results lead to an efficient algorithm for allocating the backhaul capacities in C-RAN. The performance of the proposed scheme is evaluated for practical multicell and heterogeneous networks. It is shown that multicell processing with optimized quantization noise levels across the BSs can significantly improve the performance of wireless cellular networks.

Robust speaker identification in noisy and reverberant conditions

Abstract: Robustness of speaker recognition systems is crucial for real-world applications, which typically contain both additive noise and room reverberation. However, the combined effects of additive noise and convolutive reverberation have been rarely studied in speaker identification (SID). This paper addresses this issue in two phases. We first remove background noise through binary masking using a deep neural network classifier. Then we perform robust SID with speaker models trained in selected reverberant conditions, on the basis of bounded marginalization and direct masking. Evaluation results show that the proposed system substantially improves SID performance over related systems in a wide range of reverberation time and signal-to-noise ratios.

Noise cancelling of MRS signals combining model-based removal of powerline harmonics and multichannel Wiener filtering

Abstract: SUMMARY The fidelity of magnetic resonance sounding signals is often severely degraded by noise, primarily electrical interference from powerline harmonics and short electromagnetic discharges. In many circumstances, the noise originates from multiple sources. We show that noise cancelling can be improved if the multiple origins of noise are taken into account. In particular, a method is developed where powerline harmonics are efficiently removed through a modelbased approach. Subsequently, standard multichannel Wiener filtering can be used to provide a further noise reduction. The performance of the method depends on the distribution of noise on the particular site of measurement. Simulations on synthetic signals embedded in real noise recordings show that the combined approach can improve the signal-to-noise ratio with an accompanying improvement in retrieval of model parameters.

Engineering Acoustics: An Introduction to Noise Control

Abstract: Perception of sound.- Fundamentals of wave propagation.- Propagation and radiation of sound.- Structure-borne sound.- Elastic isolation.- Sound absorbers.- Fundamentals of room acoustics.- Building acoustics.- Silencers.- Noise barriers.- Electro-acoustic converters for airborne sound.- Fundamentals of Active Noise Control.- Aspects and Properties of Transmitters.

SoundCompass: A Distributed MEMS Microphone Array-Based Sensor for Sound Source Localization

Abstract: Sound source localization is a well-researched subject with applications ranging from localizing sniper fire in urban battlefields to cataloging wildlife in rural areas. One critical application is the localization of noise pollution sources in urban environments, due to an increasing body of evidence linking noise pollution to adverse effects on human health. Current noise mapping techniques often fail to accurately identify noise pollution sources, because they rely on the interpolation of a limited number of scattered sound sensors. Aiming to produce accurate noise pollution maps, we developed the SoundCompass, a low-cost sound sensor capable of measuring local noise levels and sound field directionality. Our first prototype is composed of a sensor array of 52 Microelectromechanical systems (MEMS) microphones, an inertial measuring unit and a low-power field-programmable gate array (FPGA). This article presents the SoundCompass's hardware and firmware design together with a data fusion technique that exploits the sensing capabilities of the SoundCompass in a wireless sensor network to localize noise pollution sources. Live tests produced a sound source localization accuracy of a few centimeters in a 25-m2 anechoic chamber, while simulation results accurately located up to five broadband sound sources in a 10,000-m2 open field.

A Bag-of-Features approach to acoustic event detection

Abstract: The classification of acoustic events in indoor environments is an important task for many practical applications in smart environments. In this paper a novel approach for classifying acoustic events that is based on a Bag-of-Features approach is proposed. Mel and gammatone frequency cepstral coefficients that originate from psychoacoustic models are used as input features for the Bag-of representation. Rather than using a prior classification or segmentation step to eliminate silence and background noise, Bag-of-Features representations are learned for a background class. Supervised learning of codebooks and temporal coding are shown to improve the recognition rates. Three different databases are used for the experiments: the CLEAR sound event dataset, the D-CASE event dataset and a new set of smart room recordings.

An unmanned aerial vehicle (uav) for collecting audio data

Abstract: A UAV is provided to cancel background noise from audio data collected by the UAV. The UAV is provided with one or more background microphones in a proximity of one or more background noise-producing components. The UAV is also provided with one or more audio source collecting microphones. The audio data collected by the background microphones may be used to reduce or cancel interfering background noise from the audio signal detected by the audio source collecting microphone. The target audio may be captured or recorded with little or no background noise.

Evidence of the Lombard effect in fishes

Abstract: Noise can be problematic for acoustically communicating organisms due to the masking effect it has on acoustic signals. Rapid expansion of human populations, accompanied by noise that comes with industrialization and motorized transportation, poses a threat for many acoustically communicating species. Although a significant amount of effort has been made exploring the responses of organisms inhabiting marine and terrestrial environments to elevated noise levels, relatively little has been directed toward organisms inhabiting small, lotic, freshwater systems. The aim of this study was to determine what effect elevated noise levels have on acoustic signals and inter-fish distance during sound production in the Blacktail Shiner, Cyprinella venusta. We hypothesized, based on the behaviors of other vocal organisms, that C. venusta would compensate for elevated noise levels by decreasing distance between sender and receiver, increasing signal amplitude (Lombard effect), or by changing temporal patterns to increase call redundancy. Using an experimental approach, we found that C. venusta altered several acoustic components under noisy conditions. Most notably, spectral levels of acoustic signals were increased in background noise, indicating presence of the Lombard effect in fishes. Inter-fish distance was typically not different between noisy and quiet conditions, although one circumstance did show a significantly smaller inter-fish distance under noisy conditions.

Denoising Detection for the Generalized Spatial Modulation System Using Sparse Property

Abstract: Generalized spatial modulation (GSM) is a novel scheme developed from the conventional single-active antenna spatial modulation (SA-SM). The challenge of the GSM lies at the receiving end. Since more than one antenna is activated, the complexity of ML detection for GSM is much higher than that for SA-SM, especially if the number of antennas is large. Low complexity sub-optimal detections, such as ZF detection or MMSE detection, can be used to detect GSM. However, the performance of these detections is poor and cannot yet be applied in an underdetermined system. Spatial modulation has an inherent property of sparsity. However, to the best of our knowledge, this property has not been used for GSM. In this paper, we exploit this property and propose a sub-optimal detection algorithm. The traditional algorithm using sparsity is sensitive to noise and not suitable for the application to communication systems. Therefore, we employ a denoising scheme to counter the effect of the background noise.

Efficient Source Separation Algorithms for Acoustic Fall Detection Using a Microsoft Kinect

Abstract: Falls have become a common health problem among older adults. In previous study, we proposed an acoustic fall detection system (acoustic FADE) that employed a microphone array and beamforming to provide automatic fall detection. However, the previous acoustic FADE had difficulties in detecting the fall signal in environments where interference comes from the fall direction, the number of interferences exceeds FADE's ability to handle or a fall is occluded. To address these issues, in this paper, we propose two blind source separation (BSS) methods for extracting the fall signal out of the interferences to improve the fall classification task. We first propose the single-channel BSS by using nonnegative matrix factorization (NMF) to automatically decompose the mixture into a linear combination of several basis components. Based on the distinct patterns of the bases of falls, we identify them efficiently and then construct the interference free fall signal. Next, we extend the single-channel BSS to the multichannel case through a joint NMF over all channels followed by a delay-and-sum beamformer for additional ambient noise reduction. In our experiments, we used the Microsoft Kinect to collect the acoustic data in real-home environments. The results show that in environments with high interference and background noise levels, the fall detection performance is significantly improved using the proposed BSS approaches.

New Weighted Time Lag Method for the Analysis of Random Telegraph Signals

Abstract: A new method for the characterization of random telegraph signals (RTSs) is presented. The method, which is based on the time lag plot, is illustrated using Monte Carlo generated RTS traces and applied to identify the contribution of defects in multilevel RTS measured in a pMOS transistor. The results show that the new method provides a powerful and easily implementable technique to obtain the parameters of the defects responsible of multilevel RTS, even when the background noise is relevant.

Quantitative technique for robust and noise-tolerant speed measurements based on speckle decorrelation in optical coherence tomography

Abstract: Intensity-based techniques in optical coherence tomography (OCT), such as those based on speckle decorrelation, have attracted great interest for biomedical and industrial applications requiring speed or flow information. In this work we present a rigorous analysis of the effects of noise on speckle decorrelation, demonstrate that these effects frustrate accurate speed quantitation, and propose new techniques that achieve quantitative and repeatable measurements. First, we derive the effect of background noise on the speckle autocorrelation function, finding two detrimental effects of noise. We propose a new autocorrelation function that is immune to the main effect of background noise and permits quantitative measurements at high and moderate signal-to-noise ratios. At the same time, this autocorrelation function is able to provide motion contrast information that accurately identifies areas with movement, similar to speckle variance techniques. In order to extend the SNR range, we quantify and model the second effect of background noise on the autocorrelation function through a calibration. By obtaining an explicit expression for the decorrelation time as a function of speed and diffusion, we show how to use our autocorrelation function and noise calibration to measure a flowing liquid. We obtain accurate results, which are validated by Doppler OCT, and demonstrate a very high dynamic range (> 600 mm/s) compared to that of Doppler OCT (±25 mm/s). We also derive the behavior for low flows, and show that there is an inherent non-linearity in speed measurements in the presence of diffusion due to statistical fluctuations of speckle. Our technique allows quantitative and robust measurements of speeds using OCT, and this work delimits precisely the conditions in which it is accurate.

Rolling element bearing fault diagnosis based on non-local means de-noising and empirical mode decomposition

Abstract: The presence of faults in the bearings of rotating machinery is usually observed with impulses in the vibration signals. However, the vibration signals are generally non-stationary and usually contaminated by noise because of the compounded background noise present in the measuring device and the effect of interference from other machine elements. Therefore in order to enhance monitoring condition, the vibration signal needs to be properly de-noised before analysis. In this study, a novel fault diagnosis method for rolling element bearings is proposed based on a hybrid technique of non-local means (NLM) de-noising and empirical mode decomposition (EMD). An NLM which removes the noise with minimal signal distortion is first employed to eliminate or at least reduce the background noise present in the measuring device. This de-noised signal is then decomposed into a finite number of stationary intrinsic mode functions (IMF) to extract the impulsive fault features from the effect of interferences from other machine elements. Finally, envelope analyses are performed for IMFs to allow for easier detection of such characteristic fault frequencies. The results of simulated and real bearing vibration signal analyses show that the hybrid feature extraction technique of NLM de-noising, EMD and envelope analyses successfully extract impulsive features from noise signals.

A Simple Model for the Underwater Noise Source Level of Ships

Abstract: Underwater noise becomes a field of growing concern because of the possible interaction with sound vocalization of marine mammals. Modeling the effect of shipping noise being a predominant contribution worldwide requires more than statistics of measured ships in the field. This article is an attempt to characterize the underwater radiated noise level of a ship by relating spectral components of noise to naval architectural features of the ship.

Informed spatial filtering for sound extraction using distributed microphone arrays

Abstract: Hands-free acquisition of speech is required in many human-machine interfaces and communication systems. The signals received by integrated microphones contain a desired speech signal, spatially coherent interfering signals, and background noise. In order to enhance the desired speech signal, state-of-the-art techniques apply data-dependent spatial filters which require the second order statistics (SOS) of the desired signal, the interfering signals and the background noise. As the number of sources and the reverberation time increase, the estimation accuracy of the SOS deteriorates, often resulting in insufficient noise and interference reduction. In this paper, a signal extraction framework with distributed microphone arrays is developed. An expectation maximization (EM)-based algorithm detects the number of coherent speech sources and estimates source clusters using time-frequency (TF) bin-wise position estimates. Subsequently, the second order statistics (SOS) are estimated using bin-wise speech presence probability (SPP) and a source probability for each source. Finally, a desired source is extracted using a minimum variance distortionless response (MVDR) filter, a multichannel Wiener filter (MWF) and a parametric multichannel Wiener filter (PMWF). The same framework can be employed for source separation, where a spatial filter is computed for each source considering the remaining sources as interferers. Evaluation using simulated and measured data demonstrates the effectiveness of the framework in estimating the number of sources, clustering, signal enhancement, and source separation.

PLC Performance Analysis Over Rayleigh Fading Channel Under Nakagami- $m$ Additive Noise

Abstract: Power line communication (PLC) utilizes power lines for transmission of power as well as data transmission. It is an emerging field of communication for the home area network of smart grid. The performance of a PLC system is significantly affected by the additive and multiplicative power line noises; the additive noises are of two types, namely background noise and impulsive noise. Whereas, the multiplicative PLC noise leads to fading in the received signal strength. This paper provides the performance analysis of a PLC system over Rayleigh fading channel under Nakagami- $m$ distributed additive background noise assuming binary phase shift keying modulation scheme. The probability density function of the decision variable is derived. We obtain a numerically computable expression of the analytical average bit error rate of the considered system. The closed-form expression of the outage probability of the PLC system is also computed. Simulation results closely verify the validity of the derived analytical expressions.

A discriminative learning approach to probabilistic acoustic source localization

Abstract: Sound source localization algorithms commonly include assessment of inter-sensor (generalized) correlation functions to obtain direction-of-arrival estimates. Here, we present a classification-based method for source localization that uses discriminative support vector machine-learning of correlation patterns that are indicative of source presence or absence. Subsequent probabilistic modeling generates a map of sound source presence probability in given directions. Being data-driven, the method during training adapts to characteristics of the sensor setup, such as convolution effects in non-free-field situations, and to target signal specific acoustic properties. Experimental evaluation was conducted with algorithm training in anechoic single-talker scenarios and test data from several reverberant multi-talker situations, together with diffuse and real-recorded background noise, respectively. Results demonstrate that the method successfully generalizes from training to test conditions. Improvement over the best of five investigated state-of-the-art angular spectrum-based reference methods was on average about 45% in terms of relative F-measure-related error reduction.

Variational Bayesian inference for multichannel dereverberation and noise reduction

Abstract: Room reverberation and background noise severely degrade the quality of hands-free speech communication systems. In this work, we address the problem of combined speech dereverberation and noise reduction using a variational Bayesian (VB) inference approach. Our method relies on a multichannel state-space model for the acoustic channels that combines frame-based observation equations in the frequency domain with a first-order Markov model to describe the time-varying nature of the room impulse responses. By modeling the channels and the source signal as latent random variables, we formulate a lower bound on the log-likelihood function of the model parameters given the observed microphone signals and iteratively maximize it using an online expectation-maximization approach. Our derivation yields update equations to jointly estimate the channel and source posterior distributions and the remaining model parameters. An inspection of the resulting VB algorithmfor blind equalization and channel identification (VB-BENCH) reveals that the presented framework includes previously proposed methods as special cases. Finally, we evaluate the performance of our approach in terms of speech quality, adaptation times, and speech recognition results to demonstrate its effectiveness for a wide range of reverberation and noise conditions.

Robust DOA estimation of multiple speech sources

Abstract: It is challenging to determine the directions of arrival of speech signals when there are fewer sensors than sources, particularly in noisy and reverberant environments. The coherence test by Mohan et al. exploits the time-frequency sparseness of non-stationary speech signals to select more relevant time-frequency bins to estimate directions of arrival. With no prior knowledge about the incoming sources, this work proposes a combination of noise-floor tracking, onset detection and a coherence test to robustly identify time-frequency bins where only one source is dominant. After that, the largest eigenvectors of covariance matrices corresponding to these bins are clustered and the directions of arrival of the sources are estimated based on the cluster centroids. Simulation and experimental results show that this method is able to localize 8 sources with small errors using only 3 omnidirectional microphones. The proposed method is robust to background noise and reverberation.

Aeroacoustic Evaluation of Flap and Landing Gear Noise Reduction Concepts

Abstract: Aeroacoustic measurements for a semi-span, 18% scale, high-fidelity Gulfstream aircraft model are presented. The model was used as a test bed to conduct detailed studies of flap and main landing gear noise sources and to determine the effectiveness of numerous noise mitigation concepts. Using a traversing microphone array in the flyover direction, an extensive set of acoustic data was obtained in the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel with the facility in the acoustically treated open-wall (jet) mode. Most of the information was acquired with the model in a landing configuration with the flap deflected 39 deg and the main landing gear alternately installed and removed. Data were obtained at Mach numbers of 0.16, 0.20, and 0.24 over directivity angles between 56 deg and 116 deg, with 90 deg representing the overhead direction. Measured acoustic spectra showed that several of the tested flap noise reduction concepts decrease the sound pressure levels by 2 - 4 dB over the entire frequency range at all directivity angles. Slightly lower levels of noise reduction from the main landing gear were obtained through the simultaneous application of various gear devices. Measured aerodynamic forces indicated that the tested gear/flap noise abatement technologies have a negligible impact on the aerodynamic performance of the aircraft model.

Ambient Noise Recorded by a Dense Broadband Seismic Deployment in Western Iberia

Abstract: The West Iberia Lithosphere and Asthenosphere Structure (WILAS) project densely covered Portugal with broadband seismic stations for 2 yrs. Here we provide an overview of the deployment, and we characterize the network ambient noise and its sources. After explaining quality control, which includes the assessment of sensor orientation, we characterize the background noise in the short‐period (SP), microseismic, and long‐period (LP) bands. We observe daily variations of SP noise associated with anthropogenic activity. Temporary and permanent stations present very similar noise levels at all periods, except at horizontal LPs, where temporary stations record higher noise levels. We find that median noise levels are extremely homogeneous across the network in the microseismic band (3–20 s) but vary widely outside this range. The amplitudes of microseismic noise display a strong seasonal variation. The seasonality is dominated by very‐long‐period double‐frequency microseisms (8 s), probably associated with winter storms. Stacks of ambient noise amplitudes show that some microseismic noise peaks are visible across the whole ground‐motion spectrum, from 0.3 to 100 s. Periods of increased microseismic amplitudes generally correlate with ocean conditions offshore of Portugal. Some seismic records display an interesting 12 hr cycle of LP (100‐s) noise, which might be related to atmospheric tides. Finally, we use plots of power spectral density versus time to monitor changes in LP instrumental response. The method allows the identification of the exact times at which LP response changes occur, which is required to improve the understanding of this instrumental artifact and to eventually correct data. Online Material: Figures and movie illustrating the variation of seismic noise amplitudes with sensor type, time, and soil type.

PLC Performance Analysis Assuming BPSK Modulation Over Nakagami- $m$ Additive Noise

Abstract: In this letter, we derive a maximum likelihood receiver of binary phase shift keying signals over Nakagami- $m$ distributed additive noise in power line communication system. The decision variable is characterized by using copula approach. The analytical average bit error rate of the considered scheme is numerically evaluated by using the cumulative distribution function of the decision variable. It is shown by simulations that the proposed receiver performs significantly better than an existing suboptimal receiver.

Unsupervised Footwear Impression Analysis and Retrieval from Crime Scene Data

Abstract: Footwear impressions are one of the most frequently secured types of evidence at crime scenes. For the investigation of crime series they are among the major investigative notes. In this paper, we introduce an unsupervised footwear retrieval algorithm that is able to cope with unconstrained noise conditions and is invariant to rigid transformations. A main challenge for the automated impression analysis is the separation of the actual shoe sole information from the structured background noise. We approach this issue by the analysis of periodic patterns. Given unconstrained noise conditions, the redundancy within periodic patterns makes them the most reliable information source in the image. In this work, we present four main contributions: First, we robustly measure local periodicity by fitting a periodic pattern model to the image. Second, based on the model, we normalize the orientation of the image and compute the window size for a local Fourier transformation. In this way, we avoid distortions of the frequency spectrum through other structures or boundary artefacts. Third, we segment the pattern through robust point-wise classification, making use of the property that the amplitudes of the frequency spectrum are constant for each position in a periodic pattern. Finally, the similarity between footwear impressions is measured by comparing the Fourier representations of the periodic patterns. We demonstrate robustness against severe noise distortions as well as rigid transformations on a database with real crime scene impressions. Moreover, we make our database available to the public, thus enabling standardized benchmarking for the first time.

The Humming Hum: Background Noise as a Carrier of ENF Artifacts in Mobile Device Audio Recordings

Abstract: Audio forensics based on fluctuations in the electrical network frequency (ENF) has become one of the major approaches for the authentication of digital audio recordings. Yet little is known about the circumstances and preconditions under which battery-powered devices leave ENF artifacts in their recordings. Our study with multiple mobile recording devices confirms the hypothesis that background noise, generated by mains-powered electronic devices in proximity to the recording device, is a carrier of ENF artifacts. Experiments in an indoor setting suggest a very high robustness and indicate the presence of ENF artifacts even multiple rooms apart from the noise source.