Real-world Noise Research Articles

An Integrated GNSS/UWB/DR/VMM Positioning Strategy for Intelligent Vehicles

Intelligent vehicle positioning is an enabling technology for decision-making, trajectory planning, and motion control, and is a key technology in intelligent transportation systems (ITSs). In order to obtain a low-cost and high-precision positioning strategy, an integrated positioning strategy for intelligent vehicles based on the global navigation satellite system (GNSS), dead reckoning (DR), ultra-wide band (UWB), and visual map matching (VMM) was proposed in this paper. First, the error sources of the three independent positioning methods of GNSS, DR, and UWB were analyzed, and independent positioning algorithms with improved accuracy were proposed. Then, the fusion positioning algorithm of GNSS/DR/UWB with an adaptive information distribution coefficient was established using a federated Kalman filter to improve the positioning accuracy and continuity. Afterward, to avoid reliance on the previous moment, the positioning result was further corrected by VMM. Finally, offline simulation and real vehicle tests were conducted under typical working conditions with the impact of real-world noise and the real-motion states of vehicles. The results showed that the GNSS/UWB/DR/VMM positioning algorithm could effectively improve the positioning accuracy and reliability of intelligent vehicles at a low cost.

Citizen scientists and university students monitor noise pollution in cities and protected areas with smartphones.

Noise pollution can cause increased stress, cognitive impairment and illness in humans and decreased fitness and altered behavior in wildlife. Maps of noise pollution are used to visualize the distribution of noise across a landscape. These maps are typically created by taking a relatively small number of sound measurements or simulated on the basis of theoretical models. However, smartphones with inexpensive sound measuring apps can be used to monitor noise and create dense maps of real-world noise measurements. Public concern with noise can make monitoring noise pollution with smartphones an engaging and educational citizen science activity. We demonstrate a method utilizing single-day citizen science noise mapping events and a university lab to collect noise data in urban environments and protected areas. Using this approach, we collected hundreds of noise measurements with participants that we used to create noise maps. We found this method was successful in engaging volunteers and students and producing usable noise data. The described methodology has potential applications for biological research, citizen science engagement, and teaching.

A real-world noise removal with wavelet speech feature

Real-world noise signals are non-stationary, and these signals are a mixture of more than one non-stationary noise signal. Most of the conventional speech enhancement algorithms (SEAs) focus primarily on a single noise corrupted speech signal, and it is far from real-world environments. In this article, we discuss speech enhancement in real-world environments with a new speech feature. The novelty of this article is three-fold, (1) The proposed model analyzed in real-world environments. (2) The proposed model uses a discrete wavelet transform (DWT) coefficients as input features. (3) The proposed Deep Denoising Autoencoder (DDAE) designed experimentally. The result of the proposed feature compares with conventional speech features like FFT-Amplitude, Log-Magnitude, Mel frequency cepstral coefficients (MFCCs), and the Gammatone filter cepstral coefficients (GFCCs). The performance of the proposed method compared with conventional speech enhancement methods. The enhanced signal evaluated with speech quality measures, like, perceptual evaluation speech quality (PESQ), weighted spectral slope (WSS), and Log-likelihood ratio (LLR). Similarly, speech intelligibility measured with short-time objective intelligibility (STOI). The results show that the proposed SEA model with the DWT feature improves quality and intelligibility in all real-world environmental Signal-to-Noise ratio (SNR) conditions.

Indoor mobile robot navigation using deep convolutional neural network

Robot can help human in their everyday life and routine. These are not an indoor robot which was designed to perform desired task, but they can adapt to our environment by themselves and to learn from their own experiences. In this research we focus on high degree of autonomy, which is a must for social robots. For training purpose autonomous exploration and unknown environments is used along with proper algorithm so that robot can adapt to unknown environments. For testing purpose, simulation is carried with sensor fusion method, so that real world noise can be reduced and accuracy can be increased. This dissertation focuses on the intelligent robot control in autonomous navigation tasks and investigates the robot learning in following aspects. This method is based on human instinct of imitation. In this standard real time data set is provided to the robot for training purpose, it gets train from these data and generalize over all unseen potential situations and environments. Convolutional Neural Network is used to determine the probability and based on that robot can act. After acceptable number of demonstrations, robot can predict output with high accuracy and hence can acquire the independent navigation skills. State-of-the-art reinforcement learning techniques is used to train the robot via interaction with the robots. Convolutional Neural Network is also incorporated for fast generalization. Robot is train based on all past state-action pairs collected during interaction. This training model can predict output which helps robot for autonomous navigation.

Adaptive and Feature-Preserving Mesh Denoising Schemes Based on Developmental Guidance

Distinguishing among different kinds of features as well as noises on 3D mesh models is crucial for feature-preserving mesh denoising. This paper proposes to address this issue via in-depth analysis of the intermediate products of the denoising processes, and one framework is presented for raising adaptive and feature-preserving mesh denoising schemes. Firstly, by analyzing the changes of the facet normals during the denoising process, we propose the definition of developmental guidance, which helps to assess the current filtering status and predict the positions of feature and smooth regions. Then, by incorporating the guidance, we put forward one interpolation-based denoising scheme, which affords an efficient way to interpolate and recover different levels of features and is robust to severe noises. Besides, we also introduce the guidance to the optimization-based model, and the achieved global scheme is tested to be stable and robust to irregular samplings. Both the theoretical analysis and extensive experimental results on synthetic and real-world noises have demonstrated the attractive advantages of our whole framework, such as being adaptive, efficient, robust, feature-preserving, etc.

Worst-Case-Optimization Robust-MVDR Beamformer for Stereo Noise Reduction in Hearing Aids

This paper presents a robust beamformer for stereo noise reduction in hearing aid applications. The worst-case optimization method was applied to the binaural minimum-variance distortionless-response (BMVDR) beamformer, for providing robustness against parameter estimation inaccuracies. This property is essential in real-world applications, since it is well known that steering-vector and noise-correlation estimation errors may significantly degrade the BMVDR performance. It is shown that its control parameters can be designed as a function of the noisy-speech interaural-level-difference estimate. Simulation experiments for synthetic and real-world noise, as well as for a range of signal to interference ratios (SIR) were performed. For a particular set of fixed control parameters, it is shown that the mean performance of the conventional BMVDR may be improved by 0.76 wideband perceptual evaluation of speech quality (WPESQ). The proposed method is especially effective for input SIRs higher than 10 dB, which is a crucial range for hearing aid users.

Sonic Elongation: Creative Audition in Documentary Film

This article investigates documentary films in which real-world sound captured from the location shoot has been treated more creatively than the captured image, in particular, instances when real-world noises pass freely between sound and musical composition. I call this process the sonic elongation of sound from image, and of music from sound. This blurring helps the soundtrack to keep one foot in the image, thus allowing the film to retain a loose grip on the traditional nonfiction aesthetic. With reference to several recent documentary feature films, I argue that such moments rely on a confusion between hearing and listening.

An Investigation of a Feature-Level Fusion for Noisy Speech Emotion Recognition

Because one of the key issues in improving the performance of Speech Emotion Recognition (SER) systems is the choice of an effective feature representation, most of the research has focused on developing a feature level fusion using a large set of features. In our study, we propose a relatively low-dimensional feature set that combines three features: baseline Mel Frequency Cepstral Coefficients (MFCCs), MFCCs derived from Discrete Wavelet Transform (DWT) sub-band coefficients that are denoted as DMFCC, and pitch based features. Moreover, the performance of the proposed feature extraction method is evaluated in clean conditions and in the presence of several real-world noises. Furthermore, conventional Machine Learning (ML) and Deep Learning (DL) classifiers are employed for comparison. The proposal is tested using speech utterances of both of the Berlin German Emotional Database (EMO-DB) and Interactive Emotional Dyadic Motion Capture (IEMOCAP) speech databases through speaker independent experiments. Experimental results show improvement in speech emotion detection over baselines.

Binaural Codebook-Based Speech Enhancement With Atomic Speech Presence Probability

In this work, we present a universal codebook-based speech enhancement framework that relies on a single codebook to encode both speech and noise components. The atomic speech presence probability (ASPP) is defined as the probability that a given codebook atom encodes speech at a given point in time. We develop ASPP estimators based on binaural cues including the interaural phase and level difference (IPD and ILD), the interaural coherence magnitude (ICM), as well as a combined version leveraging the full interaural transfer function (ITF). We evaluate the performance of the resulting ASPP-based speech enhancement algorithms on binaural mixtures of reverberant speech and real-world noise. The proposed approach improves both objective speech quality and intelligibility over a wide range of input SNR, as measured with PESQ and binaural STOI metrics, outperforming two binaural speech enhancement benchmark methods. We show that the proposed ITF-based ASPP approach achieves a good balance of the trade-off between binaural noise reduction and binaural cue preservation.

Neither global nor local: A hierarchical robust subspace clustering for image data

In this study, we consider the problem of subspace clustering in the presence of spatially contiguous noise, occlusion, and disguise. We argue that self-expressive representation of data, which is a key characteristic of current state-of-the-art approaches, is severely sensitive to occlusions and complex real-world noises. To alleviate this problem, we highlight the importance of previously neglected local representations in improving robustness and propose a hierarchical framework that combines the robustness of local-patch-based representations and the discriminative property of global representations. This approach consists of two main steps: 1) A top-down stage, in which the input data are subject to repeated division to smaller patches and 2) a bottom-up stage, in which the low rank embedding of representation matrices of local patches in the field of view of a corresponding patch in the upper level are merged on a Grassmann manifold. This unified approach provides two key pieces of information for neighborhood graph of the corresponding patch on the upper level: cannot-links and recommended-links. This supplies a robust summary of local representations which is further employed for computing self-expressive representations using a novel weighted sparse group lasso optimization problem. Numerical results for several data sets confirm the efficiency of our approach.

Comparison and Interpretation Methods for Predictive Control of Mechanics

Objects that possess mass (e.g., automobiles, manufactured items, etc.) translationally accelerate in direct proportion to the force applied scaled by the object’s mass in accordance with Newton’s Law, while the rotational companion is Euler’s moment equations relating angular acceleration of objects that possess mass moments of inertia. Michel Chasles’s theorem allows us to simply invoke Newton and Euler’s equations to fully describe the six degrees of freedom of mechanical motion. Many options are available to control the motion of objects by controlling the applied force and moment. A long, distinguished list of references has matured the field of controlling a mechanical motion, which culminates in the burgeoning field of deterministic artificial intelligence as a natural progression of the laudable goal of adaptive and/or model predictive controllers that can be proven to be optimal subsequent to their development. Deterministic A.I. uses Chasle’s claim to assert Newton’s and Euler’s relations as deterministic self-awareness statements that are optimal with respect to state errors. Predictive controllers (both continuous and sampled-data) derived from the outset to be optimal by first solving an optimization problem with the governing dynamic equations of motion lead to several controllers (including a controller that twice invokes optimization to formulate robust, predictive control). These controllers are compared to each other with noise and modeling errors, and the many figures of merit are used: tracking error and rate error deviations and means, in addition to total mean cost. Robustness is evaluated using Monte Carlo analysis where plant parameters are randomly assumed to be incorrectly modeled. Six instances of controllers are compared against these methods and interpretations, which allow engineers to select a tailored control for their given circumstances. Novel versions of the ubiquitous classical proportional-derivative, “PD” controller, is developed from the optimization statement at the outset by using a novel re-parameterization of the optimal results from time-to-state parameterization. Furthermore, time-optimal controllers, continuous predictive controllers, and sampled-data predictive controllers, as well as combined feedforward plus feedback controllers, and the two degree of freedom controllers (i.e., 2DOF). The context of the term “feedforward” used in this study is the context of deterministic artificial intelligence, where analytic self-awareness statements are strictly determined by the governing physics (of mechanics in this case, e.g., Chasle, Newton, and Euler). When feedforward is combined with feedback per the previously mentioned method (provenance foremost in optimization), the combination is referred to as “2DOF” or two degrees of freedom to indicate the twice invocation of optimization at the genesis of the feedforward and the feedback, respectively. The feedforward plus feedback case is augmented by an online (real time) comparison to the optimal case. This manuscript compares these many optional control strategies against each other. Nominal plants are used, but the addition of plant noise reveals the robustness of each controller, even without optimally rejecting assumed-Gaussian noise (e.g., via the Kalman filter). In other words, noise terms are intentionally left unaddressed in the problem formulation to evaluate the robustness of the proposed method when the real-world noise is added. Lastly, mismodeled plants controlled by each strategy reveal relative performance. Well-anticipated results include the lowest cost, which is achieved by the optimal controller (with very poor robustness), while low mean errors and deviations are achieved by the classical controllers (at the highest cost). Both continuous predictive control and sampled-data predictive control perform well at both cost as well as errors and deviations, while the 2DOF controller performance was the best overall.

Quality Evaluation of Speech Enhancement Algorithms for Normal and Hearing Loss Listeners

The subjective quality test of the enhanced speech from different enhancement algorithms for listeners with normal hearing (NH) capability as well as listeners with hearing impairment (HI) is reported. The subjective quality evaluation of speech enhancement methods in the literature survey is mostly done targeting NH listeners and fewer attempts are observed to subjectively evaluate for HI listeners. The algorithms evaluated are from four different classes: spectral subtraction class(SS), statistical model based class (minimum mean square error), subspace class(PKLT) and auditory class (ideal binary mask using STFT, ideal binary mask using gammatone filterbank and ideal binary mask using gammachirp filterbank). The algorithms are evaluated using four types of real world noises recorded in Indian scenarios namely cafeteria, traffic, station and train at -5, 0, 5 and 10 dB SNRs. The evaluation is being done as per ITU-T P.835 standard in terms of three parameters- speech signal alone, background noise and overall quality. The noisy speech database developed in Indian regional language, Marathi, at four SNRs -5, 0, 5 and 10 dB is used for evaluation. Significant improvement is observed in ideal binary mask algorithm in terms of overall quality and signal distortion ratings for NH and HI listeners. The performance of minimum mean square error is also observed comparable with the ideal binary mask algorithm in some cases.

Pixelwise Estimation of Signal-Dependent Image Noise Using Deep Residual Learning.

In traditional image denoising, noise level is an important scalar parameter which decides how much the input noisy image should be smoothed. Existing noise estimation methods often assume that the noise level is constant at every pixel. However, real-world noise is signal dependent, or the noise level is not constant over the whole image. In this paper, we attempt to estimate the precise and pixelwise noise level instead of a simple global scalar. To the best of our knowledge, this is the first work on the problem. Particularly, we propose a deep convolutional neural network named “deep residual noise estimator” (DRNE) for pixelwise noise-level estimation. We carefully design the architecture of the DRNE, which consists of a stack of customized residual blocks without any pooling or interpolation operation. The proposed DRNE formulates the process of noise estimation as pixel-to-pixel prediction. The experimental results show that the DRNE can achieve better performance on nonhomogeneous noise estimation than state-of-the-art methods. In addition, the DRNE can bring denoising performance gains in removing signal-dependent Gaussian noise when working with recent deep learning denoising methods.

Dynamic Residual Dense Network for Image Denoising.

Deep convolutional neural networks have achieved great performance on various image restoration tasks. Specifically, the residual dense network (RDN) has achieved great results on image noise reduction by cascading multiple residual dense blocks (RDBs) to make full use of the hierarchical feature. However, the RDN only performs well in denoising on a single noise level, and the computational cost of the RDN increases significantly with the increase in the number of RDBs, and this only slightly improves the effect of denoising. To overcome this, we propose the dynamic residual dense network (DRDN), a dynamic network that can selectively skip some RDBs based on the noise amount of the input image. Moreover, the DRDN allows modifying the denoising strength to manually get the best outputs, which can make the network more effective for real-world denoising. Our proposed DRDN can perform better than the RDN and reduces the computational cost by . Furthermore, we surpass the state-of-the-art CBDNet by 1.34 dB on the real-world noise benchmark.

Invariance to background noise as a signature of non-primary auditory cortex

Despite well-established anatomical differences between primary and non-primary auditory cortex, the associated representational transformations have remained elusive. Here we show that primary and non-primary auditory cortex are differentiated by their invariance to real-world background noise. We measured fMRI responses to natural sounds presented in isolation and in real-world noise, quantifying invariance as the correlation between the two responses for individual voxels. Non-primary areas were substantially more noise-invariant than primary areas. This primary-nonprimary difference occurred both for speech and non-speech sounds and was unaffected by a concurrent demanding visual task, suggesting that the observed invariance is not specific to speech processing and is robust to inattention. The difference was most pronounced for real-world background noise—both primary and non-primary areas were relatively robust to simple types of synthetic noise. Our results suggest a general representational transformation between auditory cortical stages, illustrating a representational consequence of hierarchical organization in the auditory system.

A Theoretically Guaranteed Deep Optimization Framework for Robust Compressive Sensing MRI

Magnetic Resonance Imaging (MRI) is one of the most dynamic and safe imaging techniques available for clinical applications. However, the rather slow speed of MRI acquisitions limits the patient throughput and potential indications. Compressive Sensing (CS) has proven to be an efficient technique for accelerating MRI acquisition. The most widely used CS-MRI model, founded on the premise of reconstructing an image from an incompletely filled k-space, leads to an ill-posed inverse problem. In the past years, lots of efforts have been made to efficiently optimize the CS-MRI model. Inspired by deep learning techniques, some preliminary works have tried to incorporate deep architectures into CS-MRI process. Unfortunately, the convergence issues (due to the experience-based networks) and the robustness (i.e., lack real-world noise modeling) of these deeply trained optimization methods are still missing. In this work, we develop a new paradigm to integrate designed numerical solvers and the data-driven architectures for CS-MRI. By introducing an optimal condition checking mechanism, we can successfully prove the convergence of our established deep CS-MRI optimization scheme. Furthermore, we explicitly formulate the Rician noise distributions within our framework and obtain an extended CS-MRI network to handle the real-world nosies in the MRI process. Extensive experimental results verify that the proposed paradigm outperforms the existing state-of-theart techniques both in reconstruction accuracy and efficiency as well as robustness to noises in real scene.

Development of hearing standards for Ontario’s Constable Selection System

Objective: Develop valid and defensible hearing standards for Ontario constables to ensure safe and efficient operations.Design: Research involved three steps: (1) identification of hearing critical (HC) tasks, (2) characterisation of real-world noise environments where these tasks are performed (3) and establishment of screening criteria and protocols for determining fitness for duty.Study sample: Three panels of subject matter experts (SMEs) from different Ontario police services participated in Steps 1 and 3.Result: Fifty-one HC tasks conducted in 25 different environments were identified. Acceptable levels of speech communication in noise were based on environments with the highest frequency, importance and difficulty ratings. The ability to understand soft speech was also deemed critical. These translated into a 2 dB maximum elevation in the Noise Composite speech recognition threshold (SRT) with the Hearing-In-Noise-Test and a threshold in quiet of 35 dBA or better.Conclusions: Speech communication modelling methodology greatly facilitates the task of developing fitness for duty hearing standards, but participation of SMEs is crucial for face validity.

Enhanced Automatic Speech Recognition System Based on Enhancing Power-Normalized Cepstral Coefficients

Many new consumer applications are based on the use of automatic speech recognition (ASR) systems, such as voice command interfaces, speech-to-text applications, and data entry processes. Although ASR systems have remarkably improved in recent decades, the speech recognition system performance still significantly degrades in the presence of noisy environments. Developing a robust ASR system that can work in real-world noise and other acoustic distorting conditions is an attractive research topic. Many advanced algorithms have been developed in the literature to deal with this problem; most of these algorithms are based on modeling the behavior of the human auditory system with perceived noisy speech. In this research, the power-normalized cepstral coefficient (PNCC) system is modified to increase robustness against the different types of environmental noises, where a new technique based on gammatone channel filtering combined with channel bias minimization is used to suppress the noise effects. The TIDIGITS database is utilized to evaluate the performance of the proposed system in comparison to the state-of-the-art techniques in the presence of additive white Gaussian noise (AWGN) and seven different types of environmental noises. In this research, one word is recognized from a set containing 11 possibilities only. The experimental results showed that the proposed method provides significant improvements in the recognition accuracy at low signal to noise ratios (SNR). In the case of subway noise at SNR = 5 dB, the proposed method outperforms the mel-frequency cepstral coefficient (MFCC) and relative spectral (RASTA)–perceptual linear predictive (PLP) methods by 55% and 47%, respectively. Moreover, the recognition rate of the proposed method is higher than the gammatone frequency cepstral coefficient (GFCC) and PNCC methods in the case of car noise. It is enhanced by 40% in comparison to the GFCC method at SNR 0dB, while it is improved by 20% in comparison to the PNCC method at SNR −5dB.

Unsupervised Low Latency Speech Enhancement With RT-GCC-NMF

In this paper, we present RT-GCC-NMF: a real-time (RT), two-channel blind speech enhancement algorithm that combines the non-negative matrix factorization (NMF) dictionary learning algorithm with the generalized cross-correlation (GCC) spatial localization method. Using a pre-learned universal NMF dictionary, RT-GCC-NMF operates in a frame-by-frame fashion by associating individual dictionary atoms to target speech or background interference based on their estimated time-delay of arrivals (TDOA). We evaluate RT-GCC-NMF on two-channel mixtures of speech and real-world noise from the Signal Separation and Evaluation Campaign (SiSEC). We demonstrate that this approach generalizes to new speakers, acoustic environments, and recording setups from very little training data, and outperforms all but one of the algorithms from the SiSEC challenge in terms of overall Perceptual Evaluation methods for Audio Source Separation (PEASS) scores and compares favourably to the ideal binary mask baseline. Over a wide range of input SNRs, we show that this approach simultaneously improves the PEASS and signal to noise ratio (SNR)-based Blind Source Separation (BSS) Eval objective quality metrics as well as the short-time objective intelligibility (STOI) and extended STOI (ESTOI) objective speech intelligibility metrics. A flexible, soft masking function in the space of NMF activation coefficients offers real-time control of the trade-off between interference suppression and target speaker fidelity. Finally, we use an asymmetric short-time Fourier transform (STFT) to reduce the inherent algorithmic latency of RT-GCC-NMF from 64 ms to 2 ms with no loss in performance. We demonstrate that latencies within the tolerable range for hearing aids are possible on current hardware platforms.

Web data for computing real-world noise from civil aviation

A large amount of data on civil air traffic is now available on the web, thanks to the recent development of flight tracker websites. This paper presents a novel methodology to exploit these data for computing real-world noise around airports. The proposed approach consists of data collection and pre-processing, flight path reconstruction, aircraft noise computation using a best-practice model (ECAC Doc.29), and rendering of ground track and noise contour maps in airport areas. Applications are shown for nine European airports, where the daily air traffic reconstructed from 10,752 collected flights compares well with official records from EUROCONTROL. Among these airports, London Heathrow, Amsterdam Schiphol and Vienna-Schwechat have been considered for the validation of the present methodology, and a good agreement is found between predicted ground noise levels and available historical data, with the largest deviations being detected in the portions of the airport areas most affected by departure events. The present work constitutes the first step to harnessing the potential of web data in aviation, unlocking unprecedented possibilities for assessing the environmental impact of civil aviation and providing policy-makers with a powerful tool for developing guidelines and regulations aimed at mitigating the detrimental effects of air traffic in densely inhabited regions.