Abstract
Automatically Updated Soundmaps are maps that convey the sound rather than the visual information content of an area of interest, at a certain time instant or period. Sound features encapsulate information that can be combined with the visual features of the landscape, thus leading to useful environmental conclusions. This work aims to construct an Automatically Updated Soundmap of an area of environmental interest. A hierarchical pattern recognition approach method is proposed here, that can exploit sound recordings collected by a network of microphones. Hence, after appropriate signal processing, the large amounts of information, originally in the raw form of sound recordings, can be presented in the concise yet meaningful form of a periodically updated soundmap.
Highlights
Current research related to the environmental or ecological information of landscapes is mostly focused on their visual content, e.g., the landscape characteristics of a biotope
A hierarchical pattern recognition approach method is proposed here that can exploit sound recordings collected by a network of microphones
The sound content of the landscape is proposed as an additional information stream, aiming to produce useful audio-visual features, [1]
Summary
Current research related to the environmental or ecological information of landscapes is mostly focused on their visual content, e.g., the landscape characteristics of a biotope. The sound content of the landscape is proposed as an additional information stream, aiming to produce useful audio-visual features, [1]. An Automatically Updated Soundmap (AUS) is the map of a certain region of environmental interest at a given time instant or period, which depicts the sound content, [2,3]. The periodic construction and comparison of AUSs for the same area is a useful tool for the detection of changes in an ecosystem, [4,5,6]. To this end, a method is proposed here for the development of AUSs for an area of environmental interest. The classification scheme proceeds hierarchically from coarser to finer decisions and categorization
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