Transient Noise Research Articles

Newborn Incubators Do Not Protect from High Noise Levels in the Neonatal Intensive Care Unit and Are Relevant Noise Sources by Themselves.

Background: While meaningful sound exposure has been shown to be important for newborn development, an excess of noise can delay the proper development of the auditory cortex. Aim: The aim of this study was to assess the acoustic environment of a preterm baby in an incubator on a newborn intensive care unit (NICU). Methods: An empty but running incubator (Giraffe Omnibed, GE Healthcare) was used to evaluate the incubator frequency response with 60 measurements. In addition, a full day and night period outside and inside the incubator at the NICU of the University Hospital Zurich was acoustically analyzed. Results: The fan construction inside the incubator generates noise in the frequency range of 1.3–1.5 kHz with a weighted sound pressure level (SPL) of 40.5 dB(A). The construction of the incubator narrows the transmitted frequency spectrum of sound entering the incubator to lower frequencies, but it does not attenuate transient noises such as alarms or opening and closing of cabinet doors substantially. Alarms, as generated by the monitors, the incubator, and additional devices, still pass to the newborn. Conclusions: The incubator does protect only insufficiently from noise coming from the NICUThe transmitted frequency spectrum is changed, limiting the impact of NICU noise on the neonate, but also limiting the neonate’s perception of voices. The incubator, in particular its fan, as well as alarms from patient monitors are major sources of noise. Further optimizations with regard to the sound exposure in the NICU, as well as studies on the role of the incubator as a source and modulator, are needed to meet the preterm infants’ multi-sensory needs.

A proposal for standard methods and criteria for the assessment of residential noise complaints

Acoustical consultants often receive inquiries regarding noise complaints, particularly from occupants of multi-unit residential buildings. The noise complaints are typically regarding building services noise, other transient noises caused by the building or building elements, or due to noise from neighbours. While guidelines with respect to "acceptable" noise intrusion and levels for some sources exist, often the guideline criteria are not applicable, and the assessment must be based on proposed criteria by the acoustical consultant. This can leave uncertainty and ambiguity in terms of what is and what is not a valid noise complaint. The development of a standard procedure and criteria for the assessment of noise complaints would be a significant undertaking, but would be invaluable to acoustical engineers, multi-unit residential strata/condominium/co-op board members, property management, and the general public. This paper reviews relevant literature, outlines the major components for consideration in the development of a standard procedure and criteria, and will put forth a recommended framework for a standard approach.

A neural network based noise suppression method for transient noise control with low-complexity computation

Over the decades, the noise-suppression (NS) methods for speech enhancement (SE) have been widely utilized, including the conventional signal processing methods and the deep neural networks (DNN) methods. Although stationary-noise can be suppressed successfully using conventional or DNN methods, it is significantly challenging while suppressing the non-stationary noise, especially the transient noise. Compared to conventional NS methods, DNN NS methods may work more effectively under non-stationary noises by learning the noises' temporal-frequency characteristics. However, most DNN methods are challenging to be implemented on mobile devices due to their heavy computation complexity. Indeed, even a few low-complexity DNN methods are proposed for real-time purposes, the robustness and the generalization degrade for different types of noise. This paper proposes a single channel DNN-based NS method for transient noise with low computation complexity. The proposed method enhanced the signal-to-noise ratio (SNR) while minimizing the speech's distortion, resulting in a superior improvement of the speech quality over different noise types, including transient noise.

Use of a transient SEA for calculation of structure-borne interior noise in trains from an induction motor controlled by multi-mode PWM

This paper presents a transient SEA (Statistical Energy Analysis) approach to predict the structure-borne interior noise in trains from an induction motor controlled by multi-mode PWM (Pulse Width Modulation). Most of the induction motors installed in trains are controlled by multi-mode PWM, which switches between asynchronous and synchronous modes according to the speed to reduce switching losses. This control causes the electromagnetic forces of PWM harmonics to change, resulting in a transient interior noise depending on the vehicle's speed. In this paper, we model the bogie using FEM to calculate the transmission of the electromagnetic forces to the vehicle body through traction bars and dampers. Next, we model the vehicle body using a transient SEA to calculate transient energy in a 1/3 octave band excited by the transmitted electromagnetic forces. Finally, we restore the waveform of interior noise by applying the appropriate phase to the transient energy to auralize the analysis result. We obtained reasonable agreement by comparing the analysis results of the interior noise with the actual measurements.

The effect of sound masking on employees' acoustic comfort and performance in open-plan offices in Canada

Sound masking systems are commonly used in open-plan offices to generate a controlled minimum level of background sound, in order to decrease the signal-to-noise ratio of intrusive speech and blend out transient office noise. However, a question in the acoustical design of offices is whether the self-generated noise of occupants may alone be sufficient to provide the background sound level conditions necessary to achieve similar levels of speech privacy and acoustic comfort as sound masking systems. This study examines the relationship between occupant-perceived speech privacy and acoustic comfort under three different acoustic scenarios (no masking, controlled 42 dBA, and 47 dBA masking sound levels). The study was conducted pre-COVID-19 in two separate open-plan offices located in Quebec, Canada that at the time were close to full occupancy. Employees completed subjective questionnaires before and after each change in conditions, focusing on how the sound environment impacted their comfort and work performance during the study. Statistical results show that the occupants were significantly more satisfied during the two sound masking conditions in comparison to the no-masking condition, where only the occupant-generated and exterior/mechanical system noise was present as the background sound. Implications for open-plan offices with lower occupancy conditions post-COVID-19 are discussed.

Application of a new transient-noise analysis tool for an unmodeled gravitational-wave search pipeline

Excess transient noise events, or ‘glitches’, impact the data quality of ground-based gravitational-wave (GW) detectors and impair the detection of signals produced by astrophysical sources. Identification of the causes of these glitches is a crucial starting point for the improvement of GW signal detectability. However, glitches are the product of linear and non-linear couplings among the interrelated detector-control systems that include mitigation of ground motion and regulation of optic motion, which generally makes it difficult to find their origin. We present a new software called PyChChoo which uses time series recorded in the instrumental control systems and environmental sensors around times when glitches are present in the detector’s output to reveal essential clues about their origin. Applying PyChChoo on the most adversely affecting glitches on background triggers generated by one of unmodeled GW detection pipelines called coherent waveburst operated in the data from the LIGO detectors between January 1 2020 and February 3 2020, we find that 80% of triggers are marked as either being vetoed or unvetoed in common between our analysis and the current LIGO infrastructure.

Artificial Intelligence in Gravitational-Wave Science

Artificial intelligence gaining popularity not only in the computational engineering industry but also in fundamental science. For the realization of artificial intelligence, numerous machine learning algorithms have been introduced and tested for their applicability. Even in the field of gravitational-wave science, the application of machine learning has been widely studied to enhance conventional analyses in all disciplines from searching for gravitational-wave signals to characterizing noise transients. In this article, I briefly introduce the current status of gravitational-wave science and summarize research topics in which machine learning is applied to each discipline of gravitational-wave science.

Heart rate and startle responses in diving, captive harbour porpoises (Phocoena phocoena) exposed to transient noise and sonar.

ABSTRACTAnthropogenic noise can alter marine mammal behaviour and physiology, but little is known about cetacean cardiovascular responses to exposures, despite evidence that acoustic stressors, such as naval sonars, may lead to decompression sickness. Here, we measured heart rate and movements of two trained harbour porpoises during controlled exposure to 6–9 kHz sonar-like sweeps and 40 kHz peak-frequency noise pulses, designed to evoke acoustic startle responses. The porpoises initially responded to the sonar sweep with intensified bradycardia despite unaltered behaviour/movement, but habituated rapidly to the stimuli. In contrast, 40 kHz noise pulses consistently evoked rapid muscle flinches (indicative of startles), but no behavioural or heart rate changes. We conclude that the autonomous startle response appears decoupled from, or overridden by, cardiac regulation in diving porpoises, whereas certain novel stimuli may motivate oxygen-conserving cardiovascular measures. Such responses to sound exposure may contribute to gas mismanagement for deeper-diving cetaceans.

NNETFIX: an artificial neural network-based denoising engine for gravitational-wave signals

Instrumental and environmental transient noise bursts in gravitational-wave (GW) detectors, or glitches, may impair astrophysical observations by adversely affecting the sky localization and the parameter estimation of GW signals. Denoising of detector data is especially relevant during low-latency operations because electromagnetic follow-up of candidate detections requires accurate, rapid sky localization and inference of astrophysical sources. NNETFIX is a machine learning, artificial neural network-based algorithm designed to estimate the data containing a transient GW signal with an overlapping glitch as though the glitch was absent. The sky localization calculated from the denoised data may be significantly more accurate than the sky localization obtained from the original data or by removing the portion of the data impacted by the glitch. We test NNETFIX in simulated scenarios of binary black hole coalescence signals and discuss the potential for its use in future low-latency LIGO-Virgo-KAGRA searches. In the majority of cases for signals with a high signal-to-noise ratio, we find that the overlap of the sky maps obtained with the denoised data and the original data is better than the overlap of the sky maps obtained with the original data and the data with the glitch removed.

Transient-Based Leak Detection and Monitoring of Water Pipes Using Complementary Ensemble Empirical Mode Decomposition (CEEMD) Method

Transient signal occurs when there is sudden change of pressure inside the pipeline especially due to pressure surge or opening and closing valve. Decomposition method applied in this study to analyze the transient signal and remove noise that contaminate the signal. In this paper, an adaptive decomposition algorithm that is Complementary Ensemble Empirical Mode Decomposition (CEEMD) proposed in order to overcome the problem occur in Hilbert-Huang Transform (HHT) method. This method takes over the Empirical Mode Decomposition (EMD) method that purpose as pre-processing method in HHT. This improvement made to overcome mode mixing and reconstruction error that lies in EMD method. CEEMD method used to extract the IMF’s component of a contaminated signal to remove undesirable noise signal. Instantaneous analysis using Hilbert Transform (HT) apply for selected IMF to locate the feature’s position along the pipeline. The result prove that proposed decomposition method show success with the percentage of error between measured and analysed distance only below that 4% for all features extracted.

Identification of Transient Noise to Reduce False Detections in Screening for Atrial Fibrillation.

Screening for atrial fibrillation (AF) with a handheld device for recording the ECG is becoming increasingly popular. The poorer signal quality of such ECGs may lead to false detection of AF, often caused by transient noise. Consequently, the need for expert review in AF screening can become extensive. A convolutional neural network (CNN) is proposed for transient noise identification in AF detection. The network is trained using the events produced by a QRS detector, classified into either true beat detections or false detections. The CNN and a low-complexity AF detector are trained and tested using the StrokeStop I database, containing 30-s ECGs from mass screening for AF in the elderly population. Performance evaluation of the CNN-based quality control using a subset of the database resulted in sensitivity, specificity, and accuracy of 96.4, 96.9, and 96.9%, respectively. By inserting the CNN before the AF detector, the false AF detections were reduced by 22.5% without any loss in sensitivity. The results show that the number of recordings calling for expert review can be significantly reduced thanks to the identification of transient noise. The reduction of false AF detections is directly linked to the time and cost spent on expert review.

Parallel Non-Monte Carlo Transient Noise Simulation

A novel parallel non-Monte Carlo (MC) transient noise analysis method for general nonlinear analysis is presented. Non-MC-based transient noise methods avoid expensive and lengthy MC iterations by describing the nonlinear system with a linear time-varying (LTV) system that can be used to calculate the variance of the waveform at every time point. However, the main bottleneck in these methods is that the LTV system is dense, strongly coupled, and significantly larger compared to the original system. To address this issue, the proposed method decouples the LTV system into homogeneous and inhomogeneous solutions that can be solved independently. The inhomogeneous solution is solved in parallel by partitioning the time interval into independent subintervals, while the complexity of the homogeneous system can be decreased significantly due to the linearity of the equations.

A new SVD-based filtering technique for operational modal analysis in the presence of harmonic excitation and noise

In most output-only-based modal identification methods, the excitation and the measurement error are assumed to be white Gaussian noise. One of the obstacles appears once the excitation has periodic nature and measurements are contaminated by colored noise. In this paper, a time-domain filtering technique is introduced for harmonic excitation/noise elimination in operational modal analysis. The procedure is based on the singular value decomposition of the vibrational response. If excitation, noise, and modal harmonics have no intersection, they will have independent counterparts within the decomposed row space. Therefore, the decomposed components are partly selected and explored in a few iterations and appropriated to the transient and steady-state response and noise contaminations. Then, the components associated with the steady-state response and noise are removed, and the transient response is used for identifying the observability matrix and modal parameters. The performance of the proposed procedure is investigated through several numerical and experimental case studies of vibrating structures, and compared with other methods. The effectiveness of the technique for eliminating the excitation and noise harmonics from the modal parameters, and reconstructing the vibration signals is verified.

(Invited) Defect Engineering for Monolithic Integration of III-V Semiconductors on Silicon Substrates

Implementation of high-mobility channel materials enables to increase the device performance due to the higher carrier mobility compared to silicon devices. III-V based materials such as GaAs and ternary systems (e.g. InxGa1-xAs alloys) are achieving high performing n-channel transistors. The strong progress in hetero-epitaxy using nano ridge engineering [1] leading to monolithic heterogeneous integration of III-V materials on silicon substrates resulted in state-of-the-art FinFETs, gate-all-around (GAA) and horizontal or vertical stacked nanowires (NW) on 300 mm Si wafers [2,3]. Very promising results have also been reported for GaN on Si triggering their use for high power and RF applications [4].One of the main challenges remains a good defect control in order not to degrade the electrical performance of devices and circuits [5]. The nucleation of extended defects is directly related to the hetero-epitaxial growth of layers with a lattice mismatch and their concentration depends on the composition of the layers such as e.g. the In content in InxGa1-xAs and the Al content in AlxGa1-xN. The defects have a direct impact on the generation and recombination lifetime and the leakage current [5], while also the low frequency noise (LFN) performance is influenced [6]. The identification of the defects (energy level ET, capture cross section and concentration) can be determined using Deep Level Transient Spectroscopy (DLTS) and low frequency noise spectroscopy by measuring LFN as a function of temperature.The goal of this work is to review the knowledge about defect engineering and to analyze the electrical activity of extended defects in III-V semiconductors on silicon substrates. Different case studies will be used to illustrate both the defect identification and the electrical activity of the defects. The impact of the used processing schemes to fabricate the devices is also discussed. It will e.g. be shown that defects in GaN/AlGaN MOSHEMTs increase the low frequency noise due to generation-recombination centers in the GaN depletion layer [7]. These traps can lead to a distortion of the current-voltage characteristics of short channel devices. DLTS studies are a powerful tool to identify traps and to investigate their impact on the leakage current of III-V on Si devices. Double correlation DLTS investigations on GaAs/InGaP/GaAs diodes pointed out that traps associated with sidewall interface states can dominate the peripheral diode leakage current. The focus of the presentation will be on the current understanding of defect engineering associated with the monolithic integration of III-V materials on Si and a discussion of recent results concerning the electrical activity of the defects and the defect modeling.[1] M. Baryshnikova et al., Crystals, 10, 330 (2020).[2] X. Sun et al., Digest VLSI Technology, T3-4 (2017).[3] A.P. Milemin et al., Microelectron. Eng., 192, 14 (2019).[4] L. Li et al., IEEE Electron Device Lett., 41, 689 (2020).[5] C. Claeys et al., ECS Solid State Electronics, 9, 03301 (2020).[6] C. Claeys et al., J. Phys. Cond. Ser., 1190, 012001 (2019).[7] K. Takakura et al., IEEE Trans. Electron Dev., 67, 3062 (2020).

All-fiber transient phase noise characterization of polarization maintaining Yb-doped fiber amplifiers

We demonstrated an all-fiber phase noise measurement technique to detect transient phase variation of polarization maintaining Yb-doped fiber amplifier based on active phase locking of an optical heterodyne detection system. The root mean square, power spectral density, and drift velocity in the phase noise were analyzed for both core-pumped single clad fiber amplifier and clad-pumped double-clad fiber amplifier. The phase noise was further analyzed in terms of the seed laser power, amplifier output power, and fiber optic component configurations. The phase drift velocity measurements at various amplifier output powers indicated the optical phase were closely related to the temperature variations.

Rapid and robust parameter inference for binary mergers

The detection rate for compact binary mergers has grown as the sensitivity of the global network of ground based gravitational wave detectors has improved, now reaching the stage where robust automation of the analyses is essential. Automated low-latency algorithms have been developed that send out alerts when candidate signals are detected. The alerts include sky maps to facilitate electromagnetic follow up observations, along with probabilities that the system might contain a neutron star, and hence be more likely to generate an electromagnetic counterpart. Data quality issues, such as loud noise transients (glitches), can adversely affect the low-latency algorithms, causing false alarms and throwing off parameter estimation. Here a new analysis method is presented that is robust against glitches, and capable of producing fully Bayesian parameter inference, including sky maps and mass estimates, in a matter of minutes. Key elements of the method are wavelet-based de-noising, penalized maximization of the likelihood during the initial search, rapid sky localization using pre-computed inner products, and heterodyned likelihoods for full Bayesian inference.

Adaptive line enhancer for nonstationary harmonic noise reduction

In this paper, we propose a frequency-domain adaptive line enhancer (ALE) to reduce nonstationary harmonic noise, such as medical equipment beeps, from a noisy speech signal captured by a single microphone. The reduction of nonstationary noise is very challenging, with the tradeoff between noise reduction and speech distortion, often resulting with much noise residuals. The proposed ALE is a combination of the commonly-used forward adaptive linear filter and a non-causal backward adaptive linear filter used together with an indicator for the presence of transient noise. The proposed combined filter results in less noise residuals while preserving the speech components. We compare the proposed approach to conventional and recent methods, and show that it can outperform these methods, achieving lower distortion, more noise reduction, and overall better speech quality and intelligibility.

Frequency Analysis of Transients in Electrochemical Noise of Superalloys Waspaloy and Ultimet

Nickel or Cobalt-based superalloys represent an important class of engineering materials, finding widespread application in critical components within the gas turbine engines used for jet propulsion and electricity generation. This research aimed at the frequency analysis of transients in electrochemical noise of Waspaloy and Ultimet superalloys, immersed in 3.5 wt.% in H2SO4 and NaCl solutions at two different temperatures, 25 and 60 °C. Localized corrosion behavior of superalloys was assessed using the electrochemical noise technique (EN) according to ASTM-G199 standard. Three different statistical methods filtered the EN signal, and the polynomial method was employed to obtain the noise resistance (Rn), the localization index (LI), skew and kurtosis, and the power spectral density analysis (PSD). Results indicate that the current and potential noise transients have a better behavior with better clarity when a polynomial is used to show a localized corrosion kurtosis for both superalloys.

Bayesian search for gravitational wave bursts in pulsar timing array data

The nanohertz frequency band explored by pulsar timing arrays provides a unique discovery space for gravitational wave (GW) signals. In addition to signals from anticipated sources, such as those from supermassive black hole binaries, some previously unimagined sources may emit transient GWs (a.k.a. bursts) with unknown morphology. Unmodeled transients are not currently searched for in this frequency band, and they require different techniques from those currently employed. Possible sources of such GW bursts in the nanohertz regime are parabolic encounters of supermassive black holes, cosmic string cusps and kinks, or other, as-yet-unknown phenomena. In this paper we present BayesHopperBurst, a Bayesian search algorithm capable of identifying generic GW bursts by modeling both coherent and incoherent transients as a sum of Morlet–Gabor wavelets. A trans-dimensional reversible jump Markov chain Monte Carlo sampler is used to select the number of wavelets best describing the data. We test BayesHopperBurst on various simulated datasets including different combinations of signals and noise transients. Its capability to run on real data is demonstrated by analyzing data of the pulsar B1855 + 09 from the NANOGrav 9 year dataset. Based on a simulated dataset resembling the NANOGrav 12.5 year data release, we predict that at our most sensitive time–frequency location we will be able to probe GW bursts with a root-sum-squared amplitude higher than ∼5 × 10−11 Hz−1/2, which corresponds to ∼40M ⊙ c 2 emitted in GWs at a fiducial distance of 100 Mpc.

High-Fidelity and High-Efficiency Digital Class-D Audio Power Amplifier

This study presents a high-fidelity and high-efficiency digital class-D audio power amplifier (CDA), which consists of digital and analog modules. To realize a compatible digital input, a fully digital audio digital-to-analog converter (DAC) is implemented on MATLAB and Xilinx System Generator, which consists of a 16x interpolation filter, a fourth-order four-bit quantized delta-sigma (ΔΣ) modulator, and a uniform-sampling pulse width modulator. The CDA utilizes the closed-loop negative feedback and loop-filtering technologies to minimize distortion. The audio DAC, which is based on a field-programmable gate array, consumes 0.128 W and uses 7100 LUTs, which achieves 11.2% of the resource utilization rate. The analog module is fabricated in a 0.18 µm BCD technology. The postlayout simulation results show that the CDA delivers an output power of 1 W with 93.3% efficiency to a 4 Ω speaker and achieves 0.0138% of the total harmonic distortion (THD) with a transient noise for a 1 kHz input sinusoidal test tone and 3.6 V supply. The output power reaches up to 2.73 W for 1% THD (with transient noise). The proposed amplifier occupies an active area of 1 mm2.