Methods - Soundscape Ecology: Principles, Patterns, Methods and Applications - page 236

of measuring the influence on local characters of the landscape (vegetation compo-
sition and structure, patch size and shape, ecotone distribution, heterogeneity and
fragmentation levels, etc.), the sonic aggregations or sonotopes, and the behavioral
effects that generate the soundtopes (Fig. 9.13 ).
Employment of digital recorders in the field poses some constraints such as the
necessity to download the mass storage memory when filled and to periodically
renew the power supply. To solve in part these technical problems the remote
download of data by using smart phones seems a promising solution, coupled to
independent powering with solar cells. In fact, the innovative use of smart phones to
record the sonic environment is just at the beginning but looks very promising.
There are different examples in the literature. For instance, a system of smart
phones has been tested around the Brisbane airport in Australia by Cai
et al. ( 2007 ), but see also Zhang et al. ( 2010 ), creating a sensor network for
monitoring.
9.7 Metrics in Soundscape Ecology
The processing of acoustic data is a very complex action when the identification of
a species is requested, but the main goal of soundscape ecology is to process data to
extract the emerging patterns in terms of complexity/information of the sonic
environment. In other words, automatic processing means to extract information
using the sound pressure approach or a more sophisticated spectral analysis of
frequency (Fig. 9.14 ). The two approaches are quite different from each other and
also the goals are different. The first approach measures the amount of sonic energy
(sound pressure/power) with particular attention to the effect of noise on human life
and generally collects this information using phonometers. The second approach to
the study of the spectral patterns created by sounds uses digital recorders and
requires a complex post-processing of the recorded sound, made in the majority
of the cases by adopting the Fourier transform algorithms.
Fig. 9.14 Schematic
representation of the
methodological approaches
to sonic environment
analysis