The Quantitative Relationship Between North Central Florida Ambient Soundscapes and Landscape Development Intensity

Published: 13 September 2019| Version 4 | DOI: 10.17632/wpb5fx6x6g.4
Jenet Dooley


The soundscapes of each site were sampled using a Fostex fr-2le field recorder and a Seinheisser ME 62 omni-directional microphone. The recordings were 30 minutes long and used a waveform format with a 24-bit depth and 48 kHz sampling rate. The recordings took place between 0900 and 1000 on week days over two years from April 2013 until March 2015. This time of day was selected for its ambient nature, avoiding known times of increased acoustic activity like dawn chorus or early morning rush hour traffic. A tripod ensured that the recording equipment was consistently 1 meter off the ground. Recordings were not taken if the wind in the area exceeded 10 mph or if it was raining. At each site, climatic conditions were documented at the time of the recording. This information was taken from real time data available online from the closest weather station. Temperature and relative humidity were confirmed on site with a psychrometer. The location of the recording was documented with a handheld GPS unit. The acoustic analysis of the data utilized different divisions of the frequency spectrum to describe the soundscapes. First, the total sampled frequency spectrum (20-20,000 Hz) was analyzed as a whole. Second, each site’s spectrogram was divided into twenty, 1 kHz wide frequency bands. The lowest frequency band ranged from 20-1000 Hz to accommodate for the frequency limits of the microphone used. Third, the two frequency regions attributed to anthropogenic and biologic sourced sounds (anthrophony, 20-2000 Hz and biophony, 2000-8000 Hz) were analyzed. The acoustic metrics used in this study to describe the soundscapes were calculated from spectrograms made with Raven Pro 1.4 (Cornell University, Ithaca, New York) software. The spectrograms used a Hann window with a discrete Fourier Transform size of 256 samples and 188 Hz grid size. The metrics were based on the power spectral density (PSD), the amount of sound power per unit frequency (dB/Hz), of the frequency-time bins in the spectrogram, which was calculated internally within Raven using Fourier Transforms (see Figure 2). Five different metrics were calculated using the PSD values from each recording: inband power, average PSD, delta power, aggregate entropy, and center frequency.



University of Florida


Remote Sensing, Noise, Disturbance Ecology, Soundscapes, Land Use, Sound