2022
DOI: 10.1016/j.apacoust.2021.108387
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A widened array of metrics (WAM) approach to characterize the urban acoustic environment; a case comparison of urban mixed-use and forest

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Cited by 16 publications
(19 citation statements)
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“…It must be noted, though, that even after the closure the ambient noise was slightly higher at the airport site than the control forest. However, the average ambient noise level at the airport locations was 46 dB(A) SPL, which is within the range of natural noise levels in a temperate forest [ 30 , 67 ]. Moreover, the mean difference in noise levels between the airport and the control site was lower than 4 dB, which is unlikely to affect the song timing.…”
Section: Discussionmentioning
confidence: 95%
“…It must be noted, though, that even after the closure the ambient noise was slightly higher at the airport site than the control forest. However, the average ambient noise level at the airport locations was 46 dB(A) SPL, which is within the range of natural noise levels in a temperate forest [ 30 , 67 ]. Moreover, the mean difference in noise levels between the airport and the control site was lower than 4 dB, which is unlikely to affect the song timing.…”
Section: Discussionmentioning
confidence: 95%
“…To bring the work of Lippold and Lawrence [39] into context, Lawrence et al [62] present a detailed case comparison between urban forests and urban mixed use using ecoacoustic indices [11] and an SPL measure to compare these two dichotomous areas in the urban acoustic environment. This study found average SPL reductions of 20 dB in forests as compared to mixed use, a pronounced morning and dusk avifauna chorus in forests that are either masked by traffic noise or simply not present in urban mixed use, greater overall amplitude during daytimes in urban mixed use than forests, and a visible reduction in amplitude in the sub-2 kHz range in forests.…”
Section: Selection Of Composite Ecoacoustic Indicators For Biophony A...mentioning
confidence: 99%
“…It has been established that combinations of AIs are more effective for predicting bioacoustic activity than individual indices (Towsey et al, 2014;Buxton et al, 2018b). On the other hand, some indices are correlated with each other (Cifuentes et al, 2021;Lawrence et al, 2022). We grouped the indices using principal component analysis, which allowed us to reduce the number of variables while retaining a significant part of the variance of the original variables (≥80% for three PCs).…”
Section: Aismentioning
confidence: 99%