Background Noise Conditions Research Articles

A multi-peak detection algorithm for FBG based on WPD-HT

An accurate multi-peak detection algorithm based on wavelet packet decomposition (WPD) denoising and Hilbert transform (HT) is proposed. WPD and wavelet threshold method are used to denoise the high-frequency part of the spectrum. HT and parabola fitting are used to divide the peak area adaptively and calculate the central wavelength. Simulation results show that compared with the other four denoising methods, this denoising algorithm can solve the problem of background noise more effectively. Compared with the Gaussian LM (Levenberg-Marquardt) algorithm, Centroid method, and polynomial fitting method, this method has higher precision. Experimental results of real-time temperature detection show that the stability is better than 0.5 pm at 0℃ and the temperature monitoring accuracy is better than 0.352℃ in the range of −20℃∼ 40℃, which is the best among the four algorithms. The computational speed is 81.2% higher than the Gauss LM algorithm. In general, this method can be applied to FBG real-time temperature monitoring and demodulation system under the condition of background noise.

Estimating Respiratory Rate From Breath Audio Obtained Through Wearable Microphones.

Respiratory rate (RR) is a clinical metric used to assess overall health and physical fitness. An individual's RR can change from their baseline due to chronic illness symptoms (e.g., asthma, congestive heart failure), acute illness (e.g., breathlessness due to infection), and over the course of the day due to physical exhaustion during heightened exertion. Remote estimation of RR can offer a cost-effective method to track disease progression and cardio-respiratory fitness over time. This work investigates a model-driven approach to estimate RR from short audio segments obtained after physical exertion in healthy adults. Data was collected from 21 individuals using microphone-enabled, near-field headphones before, during, and after strenuous exercise. RR was manually annotated by counting perceived inhalations and exhalations. A multi-task Long-Short Term Memory (LSTM) network with convolutional layers was implemented to process mel-filterbank energies, estimate RR in varying background noise conditions, and predict heavy breathing, indicated by an RR of more than 25 breaths per minute. The multi-task model performs both classification and regression tasks and leverages a mixture of loss functions. It was observed that RR can be estimated with a concordance correlation coefficient (CCC) of 0.76 and a mean squared error (MSE) of 0.2, demonstrating that audio can be a viable signal for approximating RR.Clinical relevance-The subject technology facilitates the use of accessible, aesthetically acceptable wearable headphones to provide a technologically efficient and cost-effective method to estimate respiratory rate and track cardio-respiratory fitness over time.

Auditory selective attention and speech-in-noise in cochlear implants

Auditory selective attention is a crucial mechanism for comprehending speech in noise. We investigated if cochlear implant (CI) users exhibit auditory selective attention that involves modulation of neural responses to target speech and if such attentional ability predicts their speech-in-noise performance. In our experiment, designed to assess the strength of attentional modulation, participants with normal hearing and with CI were given a pre-stimulus visual cue that directed their attention to either of two sequences in stationary background noise and asked to select a deviant syllable. We hypothesized that the amplitude of event-related-potential (ERP) would be greater when the syllable is attended if either group is capable of employing auditory selective attention and that the difference of ERP amplitude between attended and unattended trials predicts the performance in a speech-in-noise task. Our analysis showed that the amplitude of ERPs for the attended syllable was greater than that for the unattended syllable with the CI subjects, exhibiting that attention modulates CI users’ cortical responses to sounds. Moreover, the strength of attentional modulation showed a significant correlation with the same CI users’ speech-in-noise performance. These results show that attentional modulation provides a valuable neural marker for predicting CI users’ success in real-world communications.

Effects of background noise on the speech acoustics of people with aphasia

We investigated the effect of six background noise conditions (silent baseline, pink noise, monologue, lively conversation, one-sided phone call, and cocktail noise) on acoustic measures of speech during story retells in people with aphasia. Eleven individuals with aphasia and 11 age- and gender-matched control participants heard the background noise through headphones while they retold short stories. We measured mean and standard deviation of intensity and fundamental frequency (F0), and speech rate. A Matlab application computed a speaking time ratio measure (i.e., time speaking versus time pausing). With the exception of the monologue and one-sided phone call condition, both people with aphasia and control participants significantly increased their intensity and F0 in the presence of background noise, regardless of noise type. Additionally, participants with aphasia had significantly lower speaking time ratios and speaking rates than control participants. All speakers made acoustic changes while hearing background noise, likely because speech intensity rises in an effort to increase the signal-to-noise ratio, while mean F0 increases due to a presumed rise in subglottal pressure. Further research is suggested to investigate other acoustic differences, possibly at the segmental level, between speech produced in informational and energetic background noise conditions.

Perception and task performance under two simulated office background noise conditions

The acoustics of an open-office environment play an important role in people’s working performance. This study was conducted to investigate how task performance is affected by two simulated office noise environments: (1) recorded HVAC noise and (2) a series of intermittent noises, e.g., conversations, phone ringing, etc. These conditions were reproduced over a 32-loudspeaker array in an anechoic chamber to include realistic spatial placement of sound sources. Each subject was seated at the center of the array and asked to perform five cognitive tasks to evaluate effects on memory, reasoning, planning, and attention. The tasks were digit span, grammatical reasoning, tower of London, attention network test, and mental arithmetic. After each noise condition, the participants rated several items including annoyance and perceived performance. Heart rate variability (HRV) was also recorded using ECG sensors as a physiological measure of arousal. Baseline measurements were first obtained in silence, followed by a task training set in silence, and an actual test set in silence. A statistical analysis was used to investigate possible correlations between the acoustic conditions and the cognitive task results, perceptual ratings, and HRV data, respectively. Study details and the findings will be presented.

Sound Sources Recognition System Based on Convolutional Neural Network Using a New Dataset in Noisy Environment

A sound source recognition system based on a convolutional neural network (CNN) using a new dataset in a noisy environment was proposed and its performance was analyzed in this paper. The deep learning model in the system consists of a convolutional layer with max-pooling and a fully connected layer based on CNN, and classifies short audio clips of surrounding sounds. The performance of the proposed system was produced by measuring with the confusion matrix (CM) and the sound source recognition accuracy (SSRA). As a result of comparing the average SSRA of the proposed system applying the new sound dataset reflecting the background noise condition and the existing system applying the noiseless dataset, it was measured as 72–76% and 40–55%, respectively. Finally, it can be seen that the case of applying the new sound source dataset was superior to the case of applying the noiseless dataset by about 21–32% on average.

Comparing the Acoustic Behaviour of the Eastern Indian Ocean Pygmy Blue Whale on Two Australian Feeding Grounds

Long-term data of underwater passive acoustic monitoring (PAM) collected from two sites of pygmy blue whale presence within Australia, the continental shelf off Portland (38.5° S, 141.2° E) and the Perth Canyon (32° S, 115° E) were analysed to compare the acoustic behaviour of eastern Indian Ocean pygmy blue (EIOPB) whales. Pygmy blue whale song detection was consistently higher at the Perth Canyon site than at the Portland sample site. Statistical analysis found there to be a significant difference in the production of song and phrase variants between sites (p < 0.01) with a shorter two-unit (P2) song variant being more common in the Perth Canyon area, while the traditional three-unit (P3) song variant was more frequent off Portland. This was supported by manual and feature space analysis techniques. Increasing song complexity was observed in the form of phrases with broken song units, a phenomenon that was first observed at the Portland site on isolated occasions but has occurred and proliferated in the Perth Canyon area from 2016 onwards. Analysis of environmental conditions indicated that increased background noise due to multiple EIOPB whales vocalising, as well as water depth, may influence song length. This was reflected by songs made up of shorter phrases dominating in higher background noise conditions and deeper water, while longer more complex phrase types dominate in quieter, shallower conditions. Further research is recommended to isolate any potential influence of environmental factors on song production.

A new method for estimating the correlation of seismic waveforms based on the NTFT

SUMMARY We propose a new correlation function called the similarity coefficient (SC) based on the normal time–frequency transform (NTFT) to evaluate the similarity between two non-stationary seismic signals as a function of the delay time. The SC is defined in the time–frequency spectrum of the NTFT, and the instantaneous phase and amplitude of each frequency component in a signal are used to calculate the SC. Our simulation experiments demonstrate that the SC method can effectively recognize similar signals compared to the conventional normalized cross-correlation coefficient (NCC) under high background noise conditions. The SC presents good robustness in identifying similar signals and performs well in the case of an extremely low signal-to-noise ratio (SNR), which makes it suitable for detecting weak seismic signals concealed by noise. As a real application case, we use the SC method to detect quasi-Love (QL) surface waves. QL waves are scattered Love waves and are important indicators for lateral anisotropic gradients in the upper mantle. We detect the QL waves at 21 stations deployed across Japan after the 23 December 2004 Mw 8.1 Macquarie earthquake by using the SC method. Obvious QL waves are observed at 19 stations, and we locate the Love-to-Rayleigh scatterers by applying the delay times between the QL and main Love waves. Our results show that the QL wave scatterers were mostly generated in two areas: Mariana subduction and Papua New Guinea. The observations of QL waves suggest the presence of lateral gradients in anisotropy beneath those two areas. The spatial distribution of the 13 scatterers in the Mariana subduction zone agrees well with the Mariana Island Arc, and we infer that the Mariana slab may have melted and coupled with the surrounding mantle at depth.

Effects of Background Noise on Speech and Language in Young Adults.

Purpose The aim of this study was to investigate how different types of background noise that differ in their level of linguistic content affect speech acoustics, speech fluency, and language production for young adult speakers when performing a monologue discourse task. Method Forty young adults monologued by responding to open-ended questions in a silent baseline and five background noise conditions (debate, movie dialogue, contemporary music, classical music, and pink noise). Measures related to speech acoustics (intensity and frequency), speech fluency (speech rate, pausing, and disfluencies), and language production (lexical, morphosyntactic, and macrolinguistic structure) were analyzed and compared across conditions. Participants also reported on which conditions they perceived as more distracting. Results All noise conditions resulted in some change to spoken language compared with the silent baseline. Effects on speech acoustics were consistent with expected changes due to the Lombard effect (e.g., increased intensity and fundamental frequency). Effects on speech fluency showed decreased pausing and increased disfluencies. Several background noise conditions also seemed to interfere with language production. Conclusions Findings suggest that young adults present with both compensatory and interference effects when speaking in noise. Several adjustments may facilitate intelligibility when noise is present and help both speaker and listener maintain attention on the production. Other adjustments provide evidence that background noise eliciting linguistic interference has the potential to degrade spoken language even for healthy young adults, because of increased cognitive demands.

Environmental Performance of Noise Reduction System in Cogeneration Plants—A Life Cycle Assessment Study

Although one of the most harmful factors to health is noise, it is often ignored in life cycle assessment (LCA) studies. Most researches regarding noise inclusion in LCA focus on the problem of transport noise, and very few works consider the impact of noise emitted by other sources. This study addresses the effects of noise generated by cogeneration plants. The main objective is to assess the environmental performance of the noise reduction system by considering the health effects of noise due to steam discharge as well as to compare the environmental loss associated with the production and disposal of a steam silencer with the benefits resulting from its application for noise reduction. A method to determine midpoint and endpoint human health indicators for a composition of noise emitted by intermittent stationary source and background noise is developed. The results indicate that the health benefit due to noise reduction is six orders of magnitude greater than the environmental loss. Human health indicators attributable to noise reduction in the residential zones under consideration span a wide range of disability adjusted life years (DALY)—0.16–136 DALY. The greatest values occur in the densely populated areas located close to the noise source. A formula is proposed to express annoyance of the residents of individual zones depending on their distance to the steam discharge source, its sound power level, and existing background noise level. Uncertainty and sensitivity analyses have been carried out assuming other conditions. Modifications of sound power level, noise reduction efficiency, steam discharge duration, and the silencer lifetime have not affected the overall conclusion drawn.

Central auditory processing and listening effort in normal-hearing children: a pilot study

Objectives The current study aims to assess the link between central auditory processes and listening effort in a group of normal-hearing children. This will provide information over and beyond the standard central auditory processes and might have practical implications for listening situations which often require speech understanding in noisy environments. Method The current study included 35 normal-hearing children from six to eleven years old. Tympanometry, tonal audiometry, and speech audiometry was performed. Different auditory processing skills were assessed using the Frequency Pattern Test, the Staggered Spondaic Word Test, and the Dichotic Digits Test. Finally, a dual-task paradigm was used to evaluate listening effort. This paradigm consisted of the simultaneous performance of a speech-recognition task in different conditions of background noise and a visuospatial memory task using a digital Corsi Block test. Results Significant correlations between listening effort and several components of the auditory processing tests were established. Specifically, listening effort correlated significantly with some components of the SSWT as well as the DDT. Additionally, the listening effort test showed a significant age effect for the listening condition with the highest Signal-To-Noise Ratio. Conclusions Based on the current results, inclusion of listening effort in the test battery for auditory processing disorders would be valuable.

A Novel Rolling Bearing Vibration Impulsive Signals Detection Approach Based on Dictionary Learning

The localized faults of rolling bearings can be diagnosed by its vibration impulsive signals. However, it is always a challenge to extract the impulsive feature under background noise and non-stationary conditions. This paper investigates impulsive signals detection of a single-point defect rolling bearing and presents a novel data-driven detection approach based on dictionary learning. To overcome the effects harmonic and noise components, we propose an autoregressive-minimum entropy deconvolution model to separate harmonic and deconvolve the effect of the transmission path. To address the shortcomings of conventional sparse representation under the changeable operation environment, we propose an approach that combines K-clustering with singular value decomposition (K-SVD) and split-Bregman to extract impulsive components precisely. Via experiments on synthetic signals and real run-to-failure signals, the excellent performance for different impulsive signals detection verifies the effectiveness and robustness of the proposed approach. Meanwhile, a comparison with the state-of-the-art methods is illustrated, which shows that the proposed approach can provide more accurate detected impulsive signals.

Probability of detection of electric vehicles with and without added warning sounds.

Detection performance as a function of distance was measured for 16 subjects who pressed a button upon aurally detecting the approach of an electric vehicle. The vehicle was equipped with loudspeakers that broadcast one of four additive warning sounds. Other test conditions included two vehicle approach speeds [10 and 20 km/h (kph)] and two background noise conditions (55 and 60 dBA). All of the test warning sounds were designed to be compliant with FMVSS 141 proposed regulations in regard to the overall sound pressure levels around the vehicle and in 1/3 octave band levels. Previous work has provided detection results as average vehicle detection distance. This work provides the results as probability of detection (Pd) as a function of distance. The curves provide insight into the false alarm rate when the vehicle is far away from the listeners as well and the Pd at the mean detection distance. Results suggest that, although the test sounds provide an average detection distance that exceeds the National Highway Traffic Safety Administration minimum at the two test speeds, Pd is not always 100% at those distances, particularly at the 10 kph. At the higher speed of 20 kph, the tire-road interaction noise becomes dominant, and the detection range is greatly extended.

A Recursive Denoising Learning for Gear Fault Diagnosis Based on Acoustic Signal in Real Industrial Noise Condition

Acoustic-based diagnosis (ABD) is a promising method for machinery fault detection due to its ability to overcome the limitation of vibration measurement through non-contact measurement by air couple. However, most of the ABD approaches are not widely used in real industrial scenario due to the limitation of strong and highly non-stationary background noise interference. To address the shortcoming, a novel ABD method based on recursive denoising learning (RDL) is proposed in this article. In the proposed method, a new multistage attention mechanism is designed as the fundament of RDL for adaptive tracking and estimating non-stationary industrial background noise and automatic suppressing noise. Based on the multistage attention mechanism, a novel recursive learning strategy is introduced to further improve the performance of noise suppression by recursive tracking noise component and gradual denoising in coarse-to-fine manner. Then, an information fusion method, which is based on an improved tiny-shuffle network (TSN), is adopt to increase the discriminative representation of fault feature through fusion of multi-channel denoising information for improving diagnosis accuracy. Afterward, an RDL-based fault diagnosis method is finally obtained by combining with a standard fault diagnosis model, and it eventually achieves good performance for detection gear fault pattern in noise interference environment. The experimental results in both real industrial background noise condition and additive white Gaussian noise (AWGN) condition with different signal-to-noise ratios (SNRs) indicate that the proposed method performs better than all other popular methods in noise suppression and gear fault pattern detection, which verify the effectiveness of the proposed ABD method in dealing with gear fault diagnosis task under noise condition.

The Effects of Dysphonic Voice on Speech Intelligibility in Cantonese-Speaking Adults.

Purpose This study aims to investigate the effects of dysphonic voice on speech intelligibility in Cantonese-speaking adults. Method Speech recordings from three speakers with dysphonia secondary to phonotrauma and three speakers with healthy voices were presented to 30 healthy listeners (15 men and 15 women; M age = 22.7 years) under six noise conditions (signal-to-noise ratio [SNR] -10, SNR -5, SNR 0, SNR +5, SNR +10) and quiet conditions. The speech recordings were composed of sentences with five different lengths: five syllables, eight syllables, 10 syllables, 12 syllables, and 15 syllables. The effects of speaker's voice quality, background noise condition, and sentence length on speech intelligibility were examined. Speech intelligibility scores were calculated based on the listener's correct judgment of the number of syllables heard as a percentage of the total syllables in each stimulus. Results Dysphonic voices, as compared to healthy voices, were significantly more affected by background noise. Speech presented with dysphonic voices was significantly less intelligible than speech presented with healthy voices under unfavorable SNR conditions (SNR -10, SNR -5, and SNR 0 conditions). However, there was no sufficient evidence to suggest effects of sentence length on intelligibility, regardless of the speaker's voice quality or the level of background noise. Conclusions This study provides empirical data on the impacts of dysphonic voice on speech intelligibility in Cantonese speakers. The findings highlight the importance of educating the public about the impacts of voice quality and background noise on speech intelligibility and the potential of compensatory strategies that specifically address these barriers. Supplemental Material https://doi.org/10.23641/asha.13335926.

Induced brain magnetic activities related to salient birdsong under noisy conditions.

Birdsong sounds are often used to inform visually-challenged people about the presence of basic infrastructures, and therefore need to be salient in noisy urban environments. How salient sounds are processed in the brain could inform us about the optimal birdsong in such environments. However, brain activity related to birdsong salience is not yet known. Oscillatory magnetoencephalographic (MEG) activities and subjective salience induced by six birdsongs under three background noise conditions were measured. Thirteen participants completed the MEG measurements and 11 participants took part in the paired-comparison tests. We estimated the power of induced oscillatory activities, and explored the relationship between subjective salience of birdsongs and the power of induced activities using sparse regression analysis. According to sparse regression analysis, the subjective salience was explained by the power of induced alpha (8-13 Hz) in the frontal region, induced beta (13-30 Hz) in the occipital region, and induced gamma (30-50 Hz) in the parietal region. The power of the frontal alpha and parietal gamma activities significantly varied across both birds and noise conditions. These results indicate that frontal alpha activity is related to the salience of birdsong and that parietal gamma activity is related to differences in salience across noisy environments. These results suggest that salient birdsong under a noisy environment activates the bottom-up attention network.

Analysis of Language Errors in Speakers Who Are Bilingual Under Quiet and Background Noise Conditions

Purpose This study discusses the ways in which the presence of background noise may adversely affect bilingual students' ability to repeat speech, specifically, whether the presence of background noise increases lexical, grammatical, omission, and other errors on the AzBio Sentence Test. Method Participants consisted of 15 monolingual English (first language) speakers and 41 bilingual (second language [L2]) speakers from Spanish, Portuguese, and Korean language groups, ranging from students who learned English after the age of 4 years to adulthood. Participants completed the AzBio Sentence Test in both quiet and background noise environments. A language analysis was conducted, and errors were classified under categories of lexical, grammatical, omissions, and other. Results The results of this study show that L2 speakers made more lexical, grammatical, omissions, and other errors than first language speakers on the AzBio Sentence Test. In background noise, all bilingual language groups made more errors than monolinguals. Portuguese speakers notably made more omissions than other language groups. This study also shows that participants who were introduced to English at a younger age made fewer errors. Conclusions In conclusion, our results corroborate previous research, indicating that bilingual speakers have more difficulty accurately interpreting speech in the presence of background noise. These findings have implications for classroom instruction considering the statistically higher percentage of omissions made by L2 speakers.

Interaction of Background Noise and Auditory Hallucinations on Phonemic Mismatch Negativity (MMN) and P3a Processing in Schizophrenia.

Auditory hallucinations (AHs) are among the cardinal symptoms of schizophrenia (SZ). During the presence of AHs aberrant activity of auditory cortices have been observed, including hyperactivation during AHs alone and hypoactivation when AHs are accompanied by a concurrent external auditory competitor. Mismatch negativity (MMN) and P3a are common ERPs of interest within the study of SZ as they are robustly reduced in the chronic phase of the illness. The present study aimed to explore whether background noise altered the auditory MMN and P3a in those with SZ and treatment-resistant AHs.MethodsMMN and P3a were assessed in 12 hallucinating patients (HPs), 11 non-hallucinating patients (NPs) and 9 healthy controls (HCs) within an auditory oddball paradigm. Standard (P = 0.85) and deviant (P = 0.15) stimuli were presented during three noise conditions: silence (SL), traffic noise (TN), and wide-band white noise (WN).ResultsHPs showed significantly greater deficits in MMN amplitude relative to NPs in all background noise conditions, though predominantly at central electrodes. Conversely, both NPs and HPs exhibited significant deficits in P3a amplitude relative to HCs under the SL condition only.SignificanceThese findings suggest that the presence of AHs may specifically impair the MMN, while the P3a appears to be more generally impaired in SZ. That MMN amplitudes are specifically reduced for HPs during background noise conditions suggests HPs may have a harder time detecting changes in phonemic sounds during situations with external traffic or “real-world” noise compared to NPs.

Adaptability and Anti-Noise Capacity Enhancement for ϕ-OTDR With Deep Learning

A disturbance detection method with deep learning is proposed and experimentally demonstrated for φ-OTDR. Compared with typical methods, better performance can be intensified by entirely using the multi-dimensional features of sensing data. The adaptability and anti-noise capacity are greatly advanced with the proposed method. In experiments, an ultra-high SNR of 53.98 dB can be obtained in the best case. A spatial resolution of 1.06m and SNR of 38.49 dB are achieved simultaneously when the pulse width is modulated to the narrowest. Moreover, additive noise and multiplicative noise with a level of 0.015 were applied, respectively. The SNR can reach 35.22 dB and 42.39 dB. With this approach, the stable high SNR is unprecedentedly improved. This method provides the potential for temporal-spatial disturbance detection in φ-OTDR with strong background noise and extreme conditions.

The negative impact of wearing personal protective equipment on communication during coronavirus disease 2019.

Coronavirus disease 2019 personal protective equipment has been reported to affect communication in healthcare settings. This study sought to identify those challenges experimentally. Bamford-Kowal-Bench speech discrimination in noise performance of healthcare workers was tested under simulated background noise conditions from a variety of hospital environments. Candidates were assessed for ability to interpret speech with and without personal protective equipment, with both normal speech and raised voice. There was a significant difference in speech discrimination scores between normal and personal protective equipment wearing subjects in operating theatre simulated background noise levels (70 dB). Wearing personal protective equipment can impact communication in healthcare environments. Efforts should be made to remind staff about this burden and to seek alternative communication paradigms, particularly in operating theatre environments.