Beyond traditional wind farm noise characterisation using transfer learning.

This study proposes an approach for the characterisation and assessment of wind farm noise (WFN), which is based on extraction of acoustic features between 125 and 7500 Hz from a pretrained deep learning model (referred to as deep acoustic features). Using data measured at a variety of locations, this study shows that deep acoustic features can be linked to meaningful characteristics of the noise. This study finds that deep acoustic features can reveal an improved spatial and temporal representation of WFN compared to what is revealed using traditional spectral analysis and overall noise descriptors. These results showed that this approach is promising, and thus it could provide the basis for an improved framework for WFN assessment in the future.

Environmental noise-induced cardiovascular responses during sleep.

STUDY OBJECTIVES: This study was designed to test the utility of cardiovascular responses as markers of potentially different environmental noise disruption effects of wind farm compared to traffic noise exposure during sleep. METHODS: Twenty participants underwent polysomnography. In random order, and at six sound pressure levels from 33 dBA to 48 dBA in 3 dB increments, three types of wind farm and two types of road traffic noise recordings of 20-s duration were played during established N2 or deeper sleep, each separated by 20 s without noise. Each noise sequence also included a no-noise control. Electrocardiogram and finger pulse oximeter recorded pulse wave amplitude changes from the pre-noise onset baseline following each noise exposure and were assessed algorithmically to quantify the magnitude of heart rate and finger vasoconstriction responses to noise exposure. RESULTS: Higher sound pressure levels were more likely to induce drops in pulse wave amplitude. Sound pressure levels as low as 39 dBA evoked a pulse wave amplitude response (Odds ratio [95% confidence interval]; 1.52 [1.15, 2.02]). Wind farm noise with amplitude modulation was less likely to evoke a pulse wave amplitude response than the other noise types, but warrants cautious interpretation given low numbers of replications within each noise type. CONCLUSIONS: These preliminary data support that drops in pulse wave amplitude are a particularly sensitive marker of noise-induced cardiovascular responses during. Larger trials are clearly warranted to further assess relationships between recurrent cardiovascular activation responses to environmental noise and potential long-term health effects.

A Novel Electroencephalogram-derived Measure of Disrupted Delta Wave Activity during Sleep Predicts All-Cause Mortality Risk.

Rationale: Conventional markers of sleep disturbance, based on manual electroencephalography scoring, may not adequately capture important features of more fundamental electroencephalography-related sleep disturbance. Objectives: This study aimed to determine if more comprehensive power-spectral measures of delta wave activity during sleep are stronger independent predictors of mortality than conventional sleep quality and disturbance metrics. Methods: Power spectral analysis of the delta frequency band and spectral entropy-based markers to quantify disruption of electroencephalography delta power during sleep were performed to examine potential associations with mortality risk in the Sleep Heart Health Study cohort (N = 5,804). Adjusted Cox proportional hazard models were used to determine the association between disrupted delta wave activity at baseline and all-cause mortality over an approximately 11-year follow-up period. Results: Disrupted delta electroencephalography power during sleep was associated with a 32% increased risk of all-cause mortality compared with no fragmentation (hazard ratio, 1.32 [95% confidence interval, 1.14-1.50]), after adjusting for total sleep time and other clinical and lifestyle-related covariates, including sleep apnea. The association was of similar magnitude to a reduction in total sleep time from 6.5 hours to 4.25 hours. Conventional measures of sleep quality, including wake after sleep onset and arousal index, were not predictive of all-cause mortality. Conclusions: Delta wave activity disruption during sleep is strongly associated with all-cause mortality risk, independent of traditional potential confounders. Future investigation into the potential role of delta sleep disruption on other specific adverse health consequences such as cardiometabolic, mental health, and safety outcomes has considerable potential to provide unique neurophysiological insight.

Amplitude modulated wind farm noise relationship with annoyance: A year-long field study.

This paper presents results from a one-year study of indoor annoyance and self-reported sleep times for two participants located near different wind farms. Continuous measurements of outdoor and indoor noise and meteorological conditions were taken at each location for the duration of the study. In at least 50% of the annoyance recordings, participants described noise as "swish" or "swoosh." Furthermore, the majority of the annoyance recordings occurred at nighttime and in the early morning. The third quartile of A-weighted indoor sound pressure level [SPL(A)], between 27 and 31 dBA, was associated with an 88% increased probability of annoyance compared to the lowest reference quartile, which was between 12 and 22 dBA [odds ratio and 95% confidence intervals, 7.72 (2.61,22.8), p < 0.001]. The outdoor SPL(A) was also predictive of annoyance but only between 40 and 45 dBA. The outdoor prevalence of amplitude modulation (AM), defined as the percentage of time that AM was detectable by an algorithm for each annoyance period, was also associated with annoyance. Self-reported sleep efficiency (time spent asleep relative to time in bed available for sleep) was significantly associated with nighttime annoyance (ß = -0.66, p = 0.02) but only explained a small fraction of the variance (R2 = 5%).