Suppress Background Noise Research Articles

Geodesic weighted Bayesian model for saliency optimization

Bottom-up methods and general Bayesian framework for saliency detection commonly suffer from two drawbacks. First, they are sensitive to background noise, thus background regions similar to objects are also highlighted. Second, they only consider appearance features and thus object with several different parts will not be highlighted uniformly. In this paper, we propose a novel and unified geodesic weighted Bayesian model which considers spatial relationship by reformulating Bayes’ formula. First, we infer a more precise initial salient regions via fully connected CRF model. Second, to highlight the whole object uniformly, we learn a robust measure of region similarity which describes the probability of two regions belonging to the same object, so regions belonging to the same object will be given similar saliency value. Third, using our learnt region similarity as edge weight, we construct an undirected weighted graph to compute geodesic distance of regions. Regions with short geodesic distance from initial salient regions will be attached more importance, thus suppressing background noise. By using results of existing methods as prior distribution, our model can integrate into all methods and improve their performance. Experiments on benchmark datasets demonstrate that our model can significantly improve the quality of saliency detection.

Cortical Evoked Potentials and Hearing Aids in Individuals with Auditory Dys-Synchrony.

The purpose of the present study was to investigate the relationship between cortical processing of speech and benefit from hearing aids in individuals with auditory dys-synchrony. Data were collected from 38 individuals with auditory dys-synchrony. Participants were selected based on hearing thresholds, middle ear reflexes, otoacoustic emissions, and auditory brain stem responses. Cortical-evoked potentials were recorded for click and speech. Participants with auditory dys-synchrony were fitted with bilateral multichannel wide dynamic range compression hearing aids. Aided and unaided speech identification scores for 40 words were obtained for each participant. Hierarchical cluster analysis using Ward's method clearly showed four subgroups of participants with auditory dys-synchrony based on the hearing aid benefit score (aided minus unaided speech identification score). The difference in the mean aided and unaided speech identification scores was significantly different in participants with auditory dys-synchrony. However, the mean unaided speech identification scores were not significantly different between the four subgroups. The N2 amplitude and P1 latency of the speech-evoked cortical potentials were significantly different between the four subgroups formed based on hearing aid benefit scores. The results indicated that subgroups of individuals with auditory dys-synchrony who benefit from hearing aids exist. Individuals who benefitted from hearing aids showed decreased N2 amplitudes compared with those who did not. N2 amplitude is associated with greater suppression of background noise while processing speech.

Inkjet Printed Flexible Biosensor Based on Graphene Field Effect Transistor

In this paper, we present a graphene field effect transistor (FET) fabricated on a flexible Kapton substrate using 3-D inkjet printing for use as a biosensor to detect infectious organisms. Inkjet printing process of graphene film is described with sheet resistance as low as 110 $\Omega $ /sq. To suppress background noises, the biosensors are based on intensity changes of the ac signal as a function of the biological agents’ concentration. Using the foodborne pathogen, Norovirus, as a proof-of-concept disease target, the value of S12 (i.e., the voltage gain from source to drain) at 10 GHz generates a linear response from 0.07 to 3.70 dB when the concentration of Norovirus protein increases from 0.1 to 100 $\mu \text{g}$ /ml. While further studies are needed to improve surface functionalization and sensitivity, the current study establishes a linear response over three orders of magnitude indicating that the flexible graphene FET sensor has a wide dynamic range for detection of biological targets that could ultimately be applied for detection of a variety of disease-causing pathogens.

Design of Background Noise Suppression Circuit for Mobile Phones

With the purpose of reducing the influence of background noise on the call quality of mobile phones, background noise suppression circuit is designed based on the principle of self-adaptive noise cancellation. Because this method is not involved in the nature of the noise itself, it can be used both for stationary noise cancellation and quasi-stationary noise cancellation. The working principle and circuit design of the system are introduced in detail. Simulated experiment was conducted in the lab, and its experimental results were analyzed. The experimental results show that the circuit works well with low cost, and has a broad prospect of application and popularization.

Development of the very long-range cosmic-ray muon radiographic imaging technique to explore the internal structure of an erupting volcano, Shinmoe-dake, Japan

Abstract. Muography offers us a tool to observe hazardous erupting volcanoes remotely. However, practical muographic observations of volcanoes from a distance are difficult; therefore, various observations have been performed in the vicinity (< 1.5 km) of volcano peaks to suppress background noise and enhance images. In this study, we created a muographic image directly beneath the caldera floor of the erupting Shinmoe-dake volcano in Japan by locating our muography telescope 5 km from the peak. The Shinmoe-dake volcano began to erupt on 19 January 2011 and, in less than 1 month, the ejected lava almost completely filled the caldera and completely changed the topography of the caldera floor. The resultant image shows a low-density region underneath the western part of the newly created caldera floor, which indicates the existence of a void there. After the volcano became less active in February 2011, infrequent eruptions might have left a void beneath the caldera floor, which may trigger a collapse in the future. We anticipate that our novel muography will be a practical tool for monitoring and predicting eruption sequences in the near future.

A comparative study of BA, APSO, GSA, hybrid PSOGSA and SPSO in dual channel speech enhancement

The present paper focuses on the suppression of background noise in speech signal by utilizing a powerful heuristic optimization algorithm called Bat algorithm (BA) for adaptive filtering in dual channel enhancement systems. Bat algorithm is a recently developed population based meta heuristic approach which is inspired by the hunting behavior of the bats. It is developed by Yang (2010). As a novel feature Bat algorithm is based on the echo location behavior of micro bats. BA uses the frequency tuning technique to increase the diversity of the solutions in the population, while at the same time it uses the automatic zooming and tries to balance the exploration and exploitation. A few studies have been carried out on the use of heuristics for ANC in speech enhancement by using standard particle swarm optimization (SPSO) and some of its variants till 2010. In order to extend these heuristic approaches to speech, accelerated particle swarm optimization (APSO) based enhancement approach has been proposed (Prajna et al. in Int J Speech Technol 17(4):341---351, 2014a; Prajna et al. in IJISA 6(4):1---10, 2014b, doi:10.5815/ijisa.2014.04.01; Prajna et al. in IEEE international conference on communications and signal processing (ICCSP), April 2014, pp 1457---1461, 2014c, doi:10.1109/ICCSP.2014.6950090). To overcome the problem of poor exploitation ability of APSO, another approach is proposed based on gravitational search algorithm (GSA) (Prajna et al. 2014a, b, c). To combine both the abilities of PSO and GSA algorithms, Hybrid PSOGSA algorithm is also proposed to ANC (Prajna et al. 2014a, b, c). To further improve the efficiency of adaptive filtering in ANC, by providing a dynamic balance between exploration and exploitation, BA is proposed to speech enhancement. This paper intends to present the Bat algorithm as an improved approach to ANC in speech enhancement when compared with that of SPSO, APSO, GSA and Hybrid PSOGSA based speech enhancement algorithms. The performance of all the algorithms is evaluated by computing four objective measures SNRI, PESQ, FAI and WSS, in two real world noise conditions Babble and Factory, at three different input SNR levels set at ?10, 0 and 5 dB. Simulation results prove that BA is the most successful algorithm of all the algorithms studied in this work, to suppress the background noise more effectively.

Increasing session-to-session transfer in a brain–computer interface with on-site background noise acquisition

Objective. A brain–computer interface (BCI) usually requires a time-consuming training phase during which data are collected and used to generate a classifier. Because brain signals vary dynamically over time (and even over sessions), this training phase may be necessary each time the BCI system is used, which is impractical. However, the variability in background noise, which is less dependent on a control signal, may dominate the dynamics of brain signals. Therefore, we hypothesized that an understanding of variations in background noise may allow existing data to be reused by incorporating the noise characteristics into the feature extraction framework; in this way, new session data are not required each time and this increases the feasibility of the BCI systems. Approach. In this work, we collected background noise during a single, brief on-site acquisition session (approximately 3 min) immediately before a new session, and we found that variations in background noise were predictable to some extent. Then we implemented this simple session-to-session transfer strategy with a regularized spatiotemporal filter (RSTF), and we tested it with a total of 20 cross-session datasets collected over multiple days from 12 subjects. We also proposed and tested a bias correction (BC) in the RSTF. Main results. We found that our proposed session-to-session strategies yielded a slightly less or comparable performance to the conventional paradigm (each session training phase is needed with an on-site training dataset). Furthermore, using an RSTF only and an RSTF with a BC outperformed existing approaches in session-to-session transfers. Significance. We inferred from our results that, with an on-site background noise suppression feature extractor and pre-existing training data, further training time may be unnecessary.

Saliency detection via a unified generative and discriminative model

In this paper, we propose a visual saliency detection algorithm which incorporates both generative and discriminative saliency models into a unified framework. First, we develop a generative model by defining image saliency as the sparse coding residual based on a learned background dictionary. Second, we introduce a discriminative model by solving an optimization problem that exploits the intrinsic relevance of similar regions for regressing region-based saliency to the smooth state. Third, a weighted sum of multi-scale region-level saliency is computed as the pixel-level saliency, which generates a more continuous and smooth result. Furthermore, object location is also utilized to suppress background noise, which acts as a vital prior for saliency detection. Experimental results show that the proposed algorithm generates more accurate saliency maps and performs favorably against the state-of-the-art saliency detection methods on three publicly available datasets.

Testing of the voice communication in smart home care

AbstractThis article is aimed to describe the method of testing the implementation of voice control over operating and technical functions of Smart Home Come. Custom control over operating and technical functions was implemented into a model of Smart Home that was equipped with KNX technology. A sociological survey focused on the needs of seniors has been carried out to justify the implementation of voice control into Smart Home Care. In the real environment of Smart Home Care, there are usually unwanted signals and additive noise that negatively affect the voice communication with the control system. This article describes the addition of a sophisticated system for filtering the additive background noise out of the voice communication with the control system. The additive noise significantly lowers the success of recognizing voice commands to control operating and technical functions of an intelligent building. Within the scope of the proposed application, a complex system based on fuzzy-neuron networks, specifically the ANFIS (Adaptive Neuro-Fuzzy Interference System) for adaptive suppression of unwanted background noises was created. The functionality of the designed system was evaluated both by subjective and by objective criteria (SSNR, DTW). Experimental results suggest that the studied system has the potential to refine the voice control of technical and operating functions of Smart Home Care even in a very noisy environment.

Raman system for sensitive and selective identification of volatile organic compounds

Analysis of volatile organic compounds (VOCs) for medical and industrial applications typically requires complex and expensive mass spectrometric systems to achieve the desired sensitivity and specificity. Raman spectroscopy enables specific compound identification based on distinct spectral fingerprints but traditionally has low sensitivity. We have developed a novel Raman system that provides VOC detection in the low ppm range. VOCs from the gas phase are absorbed and enriched in a thin polymer film coated on the surface of an optical sensor element. The VOC enriched polymer film is probed with evanescent wave excitation and optimized Raman signal collection. The herein described Raman sensor is low-cost, robust, readily manufacturable, and capable of providing high sensitivity without the nanostructured sensors required for surface enhanced Raman spectroscopy. To suppress background noise and interfering spectral features while improving our ability to resolve VOC mixture spectra, we applied two-dimensional (2D) correlation analysis to time series spectra acquired during VOC absorption into the polymer film. We established proof of principle through analysis of known VOC biomarkers for selected bacterial pathogens. The system enabled VOC detection with significantly higher sensitivity than commercial Raman probes. We observed good agreement between spectra of VOCs in the thin film obtained after 2D correlation analysis, and of pure VOCs using a commercial probe. Mixture spectra were further de-convoluted based on the off-diagonal peaks observable in the 2D Raman spectrum in conjunction with segmented data analysis. We anticipate that this system can be applied to a variety of medical, industrial, and biodefense applications.

Entangled States for Improving Noise Immunity in Ultimate Measurements

We show that in some cases, in measurements at the level when quantum limits on accuracy begin to have an appreciable effect, we can use quantum entanglement of states in the studied system for extensive active suppression of background noise. We consider examples in which entanglement provides an “extra degree of freedom”, making it possible to select the desired events while rejecting the noise.

Role of the Visual and Auditory Systems in Postural Stability in Alzheimer's Disease.

Postural stability requires the integration of multisensory input information and translation into appropriate motor responses. Surprisingly, few previous studies have addressed the role of auditory input on postural stability in healthy subjects, and none has investigated this in Alzheimer's disease (AD). To assess the influence of the visual and auditory systems on postural stability in patients with AD and healthy subjects. Twenty-four patients with AD and healthy age-matched subjects were examined by kinematic postural analysis (inertia measurement units placed at the center of mass of the body) under four different conditions: stance with eyes open and eyes closed, with and without suppression of background noise (using ear defenders). The effects of visual and auditory influences were analyzed independently and in conjunction. In both groups, visual suppression had a negative impact on postural stability, while suppression of background noise, non-specifically and without spatial cues, significantly benefited postural stability. We also observed that in both groups, the positive effect of background noise suppression was insufficient to compensate for the negative effect of visual suppression, to which the patients were significantly more vulnerable. Audition, albeit less significant than vision, also plays a role in the multi-sensorial dynamic control of postural stability by the central nervous system. In everyday life, audition is likely to be a relevant factor in postural stability. This is especially relevant in AD in which, even when the peripheral sensory system is intact, the central processing is impaired and sensory dependence is re-weighted.

Cell-sensitive phase contrast microscopy imaging by multiple exposures.

We propose a novel way of imaging live cells in a Petri dish by the phase contrast microscope. By taking multiple exposures of phase contrast microscopy images on the same cell dish, we estimate a cell-sensitive camera response function which responds to cells' irradiance signals but generates a constant on non-cell background signal. The result of this new microscopy imaging is visually superior quality, which reveals the appearance details of cells and suppresses background noise near zero. Using the cell-sensitive microscopy imaging, cells' original irradiance signals are restored from all exposures and the irradiance signals on non-cell background regions are restored as a uniform constant (i.e., the imaging system is sensitive to cells only but insensitive to non-cell background). The restored irradiance signals greatly facilitate the cell segmentation by simple thresholding. The experimental results validate that high quality cell segmentation can be achieved by our approach.

The biological role of the medial olivocochlear efferents in hearing: separating evolved function from exaptation.

Cochlear outer hair cells (OHCs) are remarkable, mechanically-active receptors that determine the exquisite sensitivity and frequency selectivity characteristic of the mammalian auditory system. While there are three to four times as many OHCs compared with inner hair cells, OHCs lack a significant afferent innervation and, instead, receive a rich efferent innervation from medial olivocochlear (MOC) efferent neurons. Activation of the MOC has been shown to exert a considerable suppressive effect over OHC activity. The precise function of these efferent tracts in auditory behavior, however, is the matter of considerable debate. The most frequent functions assigned to the MOC tracts are to protect the cochlea from traumatic damage associated with intense sound and to aid the detection of signals in noise. While considerable evidence shows that interruption of MOC activity exacerbates damage due to high-level sound exposure, the well characterized MOC physiology and evolutionary studies do not support such a role. Instead, a MOC protective effect is well explained as being a byproduct of the suppressive nature of MOC action on OHC mechanical behavior. A role in the enhancement of signals in noise backgrounds, on the other hand, is well supported by (1) an extensive physiological literature (2) examination of naturally occurring environmental acoustic conditions (3) recent data from multiple laboratories showing that the MOC plays a significant role in auditory selective attention by suppressing the response to unattended or ignored stimuli. This presentation will argue that, based on the extant literature combining the suppression of background noise through MOC-mediated rapid adaptation (RA) with the suppression of non-attended signals, in concert with the corticofugal pathways descending from the auditory cortex, the MOC system has one evolved function—to increase the signal-to-noise ratio, aiding in the detection of target signals. By contrast, the MOC system role in reducing noise damage and the effects of aging in the cochlea may well represent an exaptation, or evolutionary “spandrel”.

Global feature integration based salient region detection

The goal of saliency detection is to locate the regions which are most likely to capture human׳s attention without prior knowledge of their contents. Visual saliency detection has been widely used in image processing, but it is still a challenging problem in computer vision. In this paper, we propose a salient region detection algorithm by integrating global features namely uniqueness and spatial distribution. Two measures of contrast are computed in pixel and superpixel level respectively. In order to suppress background noise, Low-level features are refined by High-level priors which are computed with the Gaussian model based on salient region. We formulate salient region detection as a binary labeling problem that separates salient region from the background. A Conditional Random Field is learned to effectively combine these refined features for salient region detection. Experimental results on the large benchmark database demonstrate the proposed method performs well when against fifteen state-of-the-art methods in terms of precision and recall.

Aberration compensation of holographic particle images using digital holographic microscopy

Characterisation of small and large-scale vortices in turbulent flows demands a system with high spatial resolution. The measurement of high spatial resolution, three-dimensional vector displacements in fluid mechanics using holography, is usually hampered by aberration. Aberration poses some problems in particle image identification due to low fidelity of real image reconstruction. Phase mismatch between the recording and the reconstruction waves was identified as the main source of aberration in this study. This paper demonstrates how aberration compensation can be achieved by cross-correlating the complex amplitude of an aberrated reconstructed object with the phase conjugate of a known reference object in the plane of the hologram (frequency space). Results favourably show significant increase in Strehl ratio and suppression of background noise that are more pronounced for particle images of 10 and 5 microns. It is clear from the work conducted that wavefront aberration measurement and compensation of holographic microscopic objects are now possible with the use of a variant digital holographic microscope.

G214 非共鳴バックグランドノイズを考慮した混合気流内のCARS顕微鏡を用いた非侵襲センシング法の開発(OS-12:先端計測による熱工学の学際的展開(2))

This study describes development of non-intrusive measurement of CO_2 concentration by using coherent anti-Stokes Raman scattering (CARS) considering non-resonant background noise from channel wall. In order to suppress the influence of non-resonant background noise on measured values, beam diameter conversion was employed for measurement of CO_2 concentration. The relationship between CO_2 concentration and CARS intensity generated from CO_2 was obtained. The effect of background noise suppression was investigated by comparing the ratio of CARS to non-resonant noise of beam whose diameter is 5 mm, 7 mm and 10 mm.

Spatial Distorted Target Recognition Based on Improved Maximum Average Correlation Height Algorithm

Spatial distorted target is very hard to be recognized for complexity and variety of targets, which has restricted the development of pattern recognition technology to a great extent. Joint transform correlator is one of the key equipments to detect and recognize distorted targets. The appearance and development of maximum average correlation height (MACH) algorithm is introduced in this paper. Based on the principle the algorithm and experimental analysis, an improved maximum average correlation height algorithm fit for joint transform correlator is proposed, which has powerful capability of suppressing background noise and widening distortion tolerance. Target images with different shapes including scale or angular distortion constitute MACH filter in frequency domain, which is projected to space domain as reference template including varieties of attitude. To show the feasibility of the algorithm, an airplane with angular distortion in sky is processed by MACH filter as an example. Simulation and optical experimental results are given in this paper. The experiments show the angular distortion tolerance can reach up to 15 degrees. The actual effect of the improved MACH filter algorithm is confirmed very well.

Scale Distorted Target Recognition Based on Machine Vision

<div> <p class="9"><span>Machine vision means to carry out measurement or judgment with machine instead of human eyes. In the field of target recognition, optical correlation technology is a main way to realize machine vision. Targets can be recognized and located with high precision</span><span> </span><span>taking advantage of optoelectronic hybrid joint </span><span>transform</span><span> correlator (OHJTC). However, when scale or angular distortion of the detected target exists relative to the reference template, the intensity of correlation peaks will decrease to a great extent, which restricted the recognition results greatly. In this paper, the development and principle of maximum average correlation height (MACH)</span><span> </span><span>algorithm is introduced. Through amounts of experiments, t</span><span>he control</span><span>ling</span><span> parameters</span><span> </span><span>of the synthesized filter</span><span> are optimized</span><span>, which makes MACH filter suppress background noise and widen recognition range of targets</span><span>. To show the feasibility of this algorithm, simulative and optical experiments of the improved MACH filter are carried out. As an example, the recognition results of a fighter target in sky</span><span> </span><span>are given, which shows </span><span>the </span><span>scale</span><span> </span><span>distortion tolerance can reach up </span><span>to </span><span>±</span><span>23%</span><span>. The actual effect of the improved MACH filter algorithm has been confirmed very well</span><span>.</span><span> <o:p></o:p></span></p> </div>

Aeroacoustic Imaging Experiments of Airframe Noise in Lined Wall Closed-Section Wind Tunnel

AbstractAcoustic imaging is an indispensable experimental aid in the design of silent aircraft. In this work, acoustic imaging is performed in a classical closed-section wind tunnel that has been extensively modified using specifically designed liners on the walls of the test section. The details of the associated acoustic modification and the aeroacoustic imaging method are introduced in this paper. The acoustic test performance is carefully evaluated by examining background noise reductions and acoustic beamforming results. The experimental outcomes suggest that the proposed acoustic modification could suppress background noise by 5 dB at 40–80 m/s test flow speeds. The beamforming results clearly identify the dominant airframe noise sources at landing gears and high-lift devices. The experimental results agree fairly well with the preceding computational and empirical predictions. The proposed acoustic imaging technique has been shown to be an effective experimental technique in identifying airframe n...