Signals In Noisy Environments Research Articles

Comparison of Information Criteria for Detection of Useful Signals in Noisy Environments.

This paper considers the appearance of indications of useful acoustic signals in the signal/noise mixture. Various information characteristics (information entropy, Jensen-Shannon divergence, spectral information divergence and statistical complexity) are investigated in the context of solving this problem. Both time and frequency domains are studied for the calculation of information entropy. The effectiveness of statistical complexity is shown in comparison with other information metrics for different signal-to-noise ratios. Two different approaches for statistical complexity calculations are also compared. In addition, analytical formulas for complexity and disequilibrium are obtained using entropy variation in the case of signal spectral distribution. The connection between the statistical complexity criterion and the Neyman-Pearson approach for hypothesis testing is discussed. The effectiveness of the proposed approach is shown for different types of acoustic signals and noise models, including colored noises, and different signal-to-noise ratios, especially when the estimation of additional noise characteristics is impossible.

Performance Evaluation of FPGA-Based LSTM Neural Networks for Pulse Signal Detection on Real-Time Radar Warning Receivers

Abstract Radar warning receivers are real-time systems used to detect emitted signals by the enemy targets. The conventional method of detecting the signal is to determine the noise floor and differentiate the signals above the noise floor by setting a threshold value. The common methodology for detecting signals in noisy environment is Constant False Alarm Rate (CFAR) detection. In CFAR methodology, threshold level is determined for a specified probability of false alarm. CFAR dictates the signal power to be detected is higher than the noise floor, i.e. signal-to-noise ratio (SNR) should be positive. To detect radar signals for negative SNR values machine learning techniques can be used. It is possible to detect radar signals for negative SNR values by Long Short-Term Memory (LSTM) Artificial Neural Network (ANN). In this study, we evaluated whether LSTM ANN can replace the CFAR algorithm for signal detection in real-time radar receiver systems. We implemented a Field Programmable Gate Array (FPGA) based LSTM ANN architecture, where pulse signal detection could be performed with 94% success rate at -5 dB SNR level. To the best of our knowledge our study is the first where LSTM ANN is implemented on FPGA for radar warning receiver signal detection.

Noise affects mate choice based on visual information via cross-sensory interference

Animal communication is often hampered by noise interference. Noise masking has primarily been studied in terms of its unimodal effect on sound information provision and use, while little is known about its cross-modal effect and how animals weigh unimodal and multimodal courtship cues in noisy environments. Here, we examined the cross-modal effects of background noise on female visual perception of mate choice and female preference for multimodal displays (sound + vocal sac) in a species of treefrog. We tested female mate choices using audio/video playbacks in the presence and absence of noise (white noise band-filtered to match or mismatch female sensitive hearing range, heterospecific chorus). Surprisingly, multimodal displays do not improve receiver performance in noise. The heterospecific chorus and white noise band-filtered to match female sensitive hearing ranges, significantly reduced female responses to the attractive visual stimuli in addition to directly impairing auditory information use. Meanwhile, the cross-modal impacts of background noise are influenced to some extent by whether the noise band matches female sensitive hearing range and the difficulty of distinguishing tasks. Our results add to the evidence for cross-modal effects of noise and are the first to demonstrate that background noise can disrupt female responses to visual information related to mate choice, which may reduce the communication efficiency of audiovisual signals in noisy environments and impose fitness consequences. This study has key ecological and evolutionary implications because it illustrates how noise influences mate choice in wildlife via cross-sensory interference, which is crucial in revealing the function and evolution of multimodal signals in noisy environments as well as informing evidence-based conservation strategies for forecasting and mitigating the multimodal impacts of noise interference on wildlife.

Inconclusives and error rates in forensic science: a signal detection theory approach

Abstract There are times when a forensic scientist may not be comfortable drawing a firm conclusion about whether a questioned sample that appears to contain useful identifying information did or did not come from a particular known source. In such cases, the forensic scientist may call the sample pair ‘inconclusive’. We suggest that signal detection theory (SDT), which is concerned with the detection of weak signals in noisy environments, provides a useful framework for understanding the role that inconclusives play in the various feature-matching forensic sciences. SDT shows that ‘inconclusive’ is often an appropriate response depending on both the strength of the signal in the samples and the thresholds adopted by the examiner. We also argue that inconclusives should not be coded as either correct or incorrect when tabulating forensic error rates.

Relationships Among Temporal Fine Structure Sensitivity, Transient Storage Capacity, and Ultra-High Frequency Hearing Thresholds in Tinnitus Patients and Normal Adults of Different Ages.

BackgroundElderlies and tinnitus patients often find it challenging to process acoustic signals in noisy environments. The sensitivity to temporal fine structure (TFS), the transient storage capacity for TFS, and the ultra-high frequency (UHF) thresholds are all associated with aging-related damage, evidenced by speech-in-noise perception deficits. In the present study, we aimed to investigate the relationships among TFS sensitivity, transient storage capacity, and UHF thresholds in tinnitus patients and normal adults of different ages.MethodsIn the present study, 38 tinnitus patients (age ranging from 21 to 65) and 23 non-tinnitus adults (age ranging from 22 to 56) were enrolled, and some of their auditory indicators were examined, including the TFS-adaptive frequency (TFS-AF), break in interaural correlation (BIAC) delay threshold, and UHF thresholds.ResultsWe found no significant difference in TFS-AF thresholds and BIAC delay thresholds between the tinnitus group and normal group, while their relationships with age were more evident in the tinnitus group. Moreover, these two tests were only significantly correlated in the tinnitus group. UHF thresholds were significantly correlated with TFS-AF thresholds only in the tinnitus group, suggesting that the UHF hearing was positively associated with the TFS sensitivity.ConclusionThese findings indicated that the influencing factors, such as tinnitus and UHF thresholds, should be fully considered when examining age-related hearing decline, because the combination of tinnitus and poor UHF hearing might play a role in affecting hearing ability, such as TFS sensitivity.

Data Analytics on Eco-Conditional Factors Affecting Speech Recognition Rate of Modern Interaction Systems

Speech-based Interaction systems contribute to the growing class of contemporary interactive techniques (Human-Computer Interactive system), which have emerged quickly in the last few years. Versatility, multi-channel synchronization, sensitivity, and timing are all notable characteristics of speech recognition. In addition, several variables influence the precision of voice interaction recognition. However, few researchers have done a significant study on the five eco-condition variables that tend to affect speech recognition rate (SRR): ambient noise, human noise, utterance speed, and frequency. The principal strategic goal of this research is to analyze the influence of the four variables mentioned earlier on SRR, and it includes many stages of experimentation on mixed noise speech data. The sparse representation-based analyzing technique is utilized to analyze the effects. Speech recognition is not noticeably affected by a person’s usual speaking pace. As a result, high-frequency voice signals are more easily recognized (∼∼98.12%) than low-frequency speech signals in noisy environments. By performing the experiments, the test results may help design the distributive controlling and commanding systems.

Many-Body Quantum Lock-In Amplifier

Achieving high-precision detection of time-dependent signals in noisy environment is a ubiquitous issue in physics and a critical task in metrology. Lock-in amplifiers are detectors that can extract alternating signals with a known carrier frequency from an extremely noisy environment. Here, we present a protocol for achieving an entanglement-enhanced lock-in amplifier via empoying many-body quantum interferometry and periodic multiple pulses. Generally, quantum interferometry includes three stages: initialization, interrogation, and readout. The many-body quantum lock-in amplifier can be achieved via adding suitable periodic multiple-$\pi$-pulse sequence during the interrogation. Our analytical results show that, by selecting suitable input states and readout operations, the frequency and amplitude of an unknown alternating field can be simultaneously extracted via population measurements. In particular, if we input spin cat states and apply interaction-based readout operations, the measurement precisions for frequency and amplitude can both approach the Heisenberg limit. Moreover, our many-body quantum amplifier is robust against extreme stochastic noises. Our study may point out a new direction for measuring time-dependent signals with many-body quantum systems, and provides a feasible way for achieving Heisenberg-limited detection of alternating signals.

Investigating well potential parameters on neural spike enhancement in a stochastic-resonance pre-emphasis algorithm

Objective. Background noise experienced during extracellular neural recording limits the number of spikes that can be reliably detected, which ultimately limits the performance of next-generation neuroscientific work. In this study, we aim to utilize stochastic resonance (SR), a technique that can help identify weak signals in noisy environments, to enhance spike detectability. Approach. Previously, an SR-based pre-emphasis algorithm was proposed, where a particle inside a 1D potential well is exerted by a force defined by the extracellular recording, and the output is obtained as the displacement of the particle. In this study, we investigate how the well shape and damping status impact the output signal-to-noise ratio (SNR). We compare the overdamped and underdamped solutions of shallow- and steep-wall monostable wells and bistable wells in terms of SNR improvement using two synthetic datasets. Then, we assess the spike detection performance when thresholding is applied on the output of the well shape-damping status configuration giving the best SNR enhancement. Main results. The SNR depends on the well-shape and damping-status type as well as the input noise level. The underdamped solution of the shallow-wall monostable well can yield to more than four orders of magnitude greater SNR improvement compared to other configurations for low noise intensities. Using this configuration also results in better spike detection sensitivity and positive predictivity than the state-of-the-art spike detection algorithms for a public synthetic dataset. For larger noise intensities, the overdamped solution of the steep-wall monostable well provides better spike enhancement than the others. Significance. The dependence of SNR improvement on the input signal noise level can be used to design a detector with multiple outputs, each more sensitive to a certain distance from the electrode. Such a detector can potentially enhance the performance of a successive spike sorting stage.

Design and Development of Adaptive filter for Real Time Signal Processing

In Communication the signal is distorted due to noise and the notch filters are used for eliminating unwanted frequencies For filtering the signal in a specific band Notch filter is used. Notch filter can pass the signal below and above notch frequency. In most of the signal processing applications frequency estimation of narrow band signals in noisy environment are required For frequency detection and tracking Adaptive IIR filter or Adaptive FIR filters can be used. Adaptive FIR Filter is more stable than Adaptive IIR filter. In most practical applications Adaptive IIR Notch Filter (AIIRNF)is used The advantage of Adaptive IIR notch filter over FIR Filter is less number of filter coefficients and less complexity in design In this paper Adaptive IIR Notch filter is designed for real time applications. The performance of designed adaptive IIR Notch filter is evaluated based on Magnitude and Frequency response.

Deep Learning-Based Portable Device for Audio Distress Signal Recognition in Urban Areas

Real-time automatic identification of audio distress signals in urban areas is a task that in a smart city can improve response times in emergency alert systems. The main challenge in this problem lies in finding a model that is able to accurately recognize these type of signals in the presence of background noise and allows for real-time processing. In this paper, we present the design of a portable and low-cost device for accurate audio distress signal recognition in real urban scenarios based on deep learning models. As real audio distress recordings in urban areas have not been collected and made publicly available so far, we first constructed a database where audios were recorded in urban areas using a low-cost microphone. Using this database, we trained a deep multi-headed 2D convolutional neural network that processed temporal and frequency features to accurately recognize audio distress signals in noisy environments with a significant performance improvement to other methods from the literature. Then, we deployed and assessed the trained convolutional neural network model on a Raspberry Pi that, along with the low-cost microphone, constituted a device for accurate real-time audio recognition. Source code and database are publicly available.

Design and analysis of improved high-speed adaptive filter architectures for ECG signal denoising

In this paper, improved high-speed adaptive filter-based denoising architectures are proposed and implemented using the Xilinx Virtex-6 series FPGA platform. The performance of the proposed DF-RDLMS, TDF-RDLMS and TF-RDLMS implementations is analyzed and compared with that of the existing adaptive filter architectures as well as state-of-the-art wavelet-based denoising architectures. The proposed adaptive filter implementations are found to perform better than existing adaptive filter architectures and wavelet-based architectures. As compared to the existing adaptive filter implementations, the proposed architectures facilitate design flexibility in choice of the step-size parameter and also allow processing of input signals in fixed-point format as well as floating-point format. Simulation results exhibit the effectiveness of the proposed architectures in efficiently denoising ECG signals in noisy environments and prove their high suitability for low-cost high-performance denoising applications in medical field. Moreover, it is observed that the proposed high-speed adaptive filter architectures require significantly less hardware as compared to state-of-the-art wavelet-based architectures.

A Novel automatic voice recognition system based on text-independent in a noisy environment

Automatic voice recognition system aims to limit fraudulent access to sensitive areas as labs. Our primary objective of this paper is to increase the accuracy of the voice recognition in noisy environment of the Microsoft Research (MSR) identity toolbox. The proposed system enabled the user to speak into the microphone then it will match unknown voice with other human voices existing in the database using a statistical model, in order to grant or deny access to the system. The voice recognition was done in two steps: training and testing. During the training a Universal Background Model as well as a Gaussian Mixtures Model: GMM-UBM models are calculated based on different sentences pronounced by the human voice (s) used to record the training data. Then the testing of voice signal in noisy environment calculated the Log-Likelihood Ratio of the GMM-UBM models in order to classify user's voice. However, before testing noise and de-noise methods were applied, we investigated different MFCC features of the voice to determine the best feature possible as well as noise filter algorithm that subsequently improved the performance of the automatic voice recognition system.

Radon spectrogram-based approach for automatic IFs separation

The separation of overlapping components is a well-known and difficult problem in multicomponent signals analysis and it is shared by applications dealing with radar, biosonar, seismic, and audio signals. In order to estimate the instantaneous frequencies of a multicomponent signal, it is necessary to disentangle signal modes in a proper domain. Unfortunately, if signal modes supports overlap both in time and frequency, separation is only possible through a parametric approach whenever the signal class is a priori fixed. In this work, time-frequency analysis and Radon transform are jointly used for the unsupervised separation of modes of a generic frequency modulated signal in noisy environment. The proposed method takes advantage of the ability of the Radon transform of a proper time-frequency distribution in separating overlapping modes. It consists of a blind segmentation of signal components in Radon domain by means of a near-to-optimal threshold operation. The inversion of the Radon transform on each detected region allows us to isolate the instantaneous frequency curves of each single mode in the time-frequency domain. Experimental results performed on constant amplitudes chirp signals confirm the effectiveness of the proposed method, opening the way for its extension to more complex frequency modulated signals.

An improved method to detect coronary artery disease using phonocardiogram signals in noisy environment

Identification of coronary artery disease (CAD) from phonocardiogram (PCG) signal is a low signal to noise ratio (SNR) problem. This study proposes a PCG based CAD detection system robust against the environmental noise that does not require additional reference signals for noise acquisition and PCG segmentation. Here, the experiments are conducted on 40 CAD and 40 normal subjects. PCG signals are recorded from a multichannel data acquisition system from four auscultation sites on the left anterior chest. While heart sounds are propagated to different auscultation sites with a certain delay, the ambient noise appearing at microphone array are not mutually time-lagged. Thus, we propose to use the imaginary part of cross power spectral density (ICPSD) to capture the spectrum of heart sounds as it is unresponsive to zero time-lagged signals. Subband based spectral features obtained from ICPSD are classified in a machine learning framework. The performance of the system is studied in the presence of babble, vehicle and white noise in which useful information were extracted from both systolic and diastolic phases of cardiac cycle. The proposed method achieves accuracy, sensitivity and specificity of 74.98%, 76.50% and 73.46%, respectively in absence of ambient noise for k-fold (k=5) cross-validation. The accuracy for 0 dB SNR in presence of white, babble and vehicle noise were 71.13%,66.47% and 69.60%, respectively. The proposed method was found to be superior in CAD classification when compared with existing noise removal based approach. The present work shows the potential of developing a PCG-based multichannel CAD detection system as an affordable point of care device for real-life use, where a certain amount of ambient noise is expected.

Combinatorial Targeting of Distributed Forebrain Networks Reverses Noise Hypersensitivity in a Model of Autism Spectrum Disorder

Autism spectrum disorder (ASD) is associated with noise hypersensitivity, the suboptimal extraction of meaningful signals in noisy environments. Because sensory filtering can involve distinct automatic and executive circuit mechanisms, however, developing circuit-specific therapeutic strategies for ASD noise hypersensitivity can be challenging. Here, we find that both of these processes are individually perturbed in one monogenic form of ASD, Ptchd1 deletion. Although Ptchd1 is preferentially expressed in the thalamic reticular nucleus during development, pharmacological rescue of thalamic perturbations in knockout (KO) mice only normalized automatic sensory filtering. By discovering a separate prefrontal perturbation in these animals and adopting a combinatorial pharmacological approach that also rescued its associated goal-directed noise filtering deficit, we achieved full normalization of noise hypersensitivity in this model. Overall, our work highlights the importance of identifying large-scale functional circuit architectures and utilizing them as access points for behavioral disease correction.

Development of Novel Hearing Aids by Using Image Recognition Technology.

Speech is easily affected by different background noise in real environment to reduce the speech intelligibility, in particular, for hearing impaired listeners. In order to improve the above issue, several hearing aids have been developed to enhance the speech signal in noisy environment. Most of current hearing aids were designed to enhance the component of speech and suppress the component of noise. However, it is difficult to separate other speech sources. Adaptive signal enhancement with the beamforming technique might improve the above issue. However, how to distinguish the location of the desired speaker effectively is still a difficult challenge for adaptive beamforming method. A novel concept of hearing aid was proposed in this study. Different from the beamforming-based hearing aids, which use the cross-correlation-coefficient method to estimate time difference of arrival (TDOA), an image recognition technology was used to estimate the location of the desired speaker to obtain the more precise TDOA. An adaptive signal enhancement was also used to enhance the noisy speech sound. From the experimental results, the proposed system could provide a smaller absolute error of TDOA less than 1.25 × 10-4 ms, and a clear speech sound from the target speaker who the user wants to listen to.

Detection of Weak Seismic Signals in Noisy Environments from Unfiltered, Continuous Passive Seismic Recordings

Robust event detection of low signal-to-noise ratio (SNR) events, such as those characterized as induced or triggered seismicity, remains a challenge. The reason is the relatively small magnitude of the events (usually less than 2 or 3 in Richter scale) and the fact that regional permanent seismic networks can only record the strongest events of a microseismic sequence. Monitoring using temporary installed short-period arrays can fill the gap of missed seismicity but the challenge of detecting weak events in long, continuous records is still present. Further, for low SNR recordings, commonly applied detection algorithms generally require pre-filtering of the data based on a priori knowledge of the background noise. Such knowledge is often not available. We present the NpD (Non-parametric Detection) algorithm, an automated algorithm which detects potential events without the requirement for pre-filtering. Events are detected by calculating the energy contained within small individual time segments of a recording and comparing it to the energy contained within a longer surrounding time window. If the excess energy exceeds a given threshold criterion, which is determined dynamically based on the background noise for that window, then an event is detected. For each time window, to characterize background noise the algorithm uses non-parametric statistics to describe the upper bound of the spectral amplitude. Our approach does not require an assumption of normality within the recordings and hence it is applicable to all datasets. We compare our NpD algorithm with the commonly commercially applied STA/LTA algorithm and another highly efficient algorithm based on Power Spectral Density using a challenging microseismic dataset with poor SNR. For event detection, the NpD algorithm significantly outperforms the STA/LTA and PSD algorithms tested, maximizing the number of detected events whilst minimizing the number of false positives.

Characteristics of the Ground-Based User Equipment of Satellite Radio Navigation System Employing Pseudolites in Noisy Environments

This article discusses the joint functioning of satellite radio navigation systems and their augmentations in the form of pseudolite networks under radio interference of various natures and intensities. The achievable precision in determining the coordinates of the object in noisy environments using and considering the estimates of the signal power level of navigational satellites, pseudolites and interference, is an indicator of the effectiveness of such joint systems. The paper refines the model of undisturbed motion of navigation satellites in terms of determining the geocentric coordinates of pseudolites (PL). A model for determining the signal levels of navigation satellites and pseudolites at the input of user equipment (UE) of satellite radio navigation systems (SRNS) was constructed. The algorithm was refined for calculating the mean square error (MSE) to determine coordinates when sharing signals of pseudolites and navigational satellites (NS) in noisy environments. The method was developed and the energy and precision characteristics of SRNS UE operated by NS and PL signals in noisy environments were determined.

Model-free fractional order differentiator based on fractional order Jacobi orthonormal functions

The aim of this paper is to design an algebraic and robust fractional order differentiator to estimate both the Riemann–Liouville and the Caputo fractional derivatives with an arbitrary order of an unknown signal in noisy environment, without knowing the model defining the signal. For this purpose, a new class of fractional order Jacobi orthonormal functions is firstly introduced. Secondly, the truncated fractional order Jacobi orthonormal series expansion is applied to filter the noisy signal, whose fractional derivative is used to estimate the desired one. Thus, the obtained differentiator is exactly given by an integral formula which depends on a set of design parameters. Thirdly, by applying the generalized Taylor's formula, some error analysis is provided. In particular, error bounds are given, which permit to study the design parameters' influence. Fourthly, a digital fractional order differentiator is deduced in discrete noisy case. Finally, by comparing with two existing fractional order differentiators, numerical results are given to illustrate the accuracy and the robustness of the proposed fractional order differentiator.

Probability Density Functions of Stationary Surface EMG Signals in Noisy Environments

The probability density function (pdf) of an electromyography (EMG) signal provides useful information for choosing an appropriate feature extraction technique. The pdf is influenced by many factors, including the level of contraction force, muscle type, and noise. In this paper, we investigated the pdfs of noisy EMG signals artificially contaminated with five different noise types: 1) Electrocardiography (ECG) interference; 2) many spurious background spikes; 3) white Gaussian noise; 4) motion artifact; and 5) power line interference at various levels of signal-to-noise ratio (SNR). In addition, we evaluated a set of statistical descriptors for identifying a noisy EMG signal from its pdf, specifically kurtosis, negentropy, L-kurtosis, and robust measures of kurtosis (KR1 and KR2). The results show that at low SNR (<5 dB), all noise types affect the statistical descriptors for the pdf of a noisy EMG signal. In addition, KR2 performs the best among these descriptors in identifying a noisy EMG signal from its pdf, because it is computed based on the quantiles of the data. As a result, it can avoid the effects of outliers resulting in the correct identification of pdf shape of noisy EMGs with all contamination types and all levels of SNR.