Band-Limited Energy Detector (BLED) Theory
Introduction
When users work with large datasets, they often seek to first identify all sounds of a particular type, then they make measurements on the selected sounds and perhaps associate annotations with the sounds to classify them as to type or source. The process of manually browsing through hours, days, or months of recordings can be daunting.
An alternative to manually browsing through large datasets is to use automatic detection. Detection is the process of finding specific sounds of interest within recordings. Often, the signals of interest are short in duration compared to the overall recording. Detection can be a useful tool when compared to the alternative process of manually browsing days of
recordings in order to find small samples of interesting sounds.
Automatic detection software may be able to reduce the amount of time that it takes to analyze a recording. Detection is accomplished through the use of a specific detector – the mechanism which runs a detection algorithm to detect specific signals of interest. Users must be aware of trade-offs that they will need to make before deciding if detection is the correct approach for their analysis.
PowerPoint presented by Russ Charif
(June 2015 Sound Analysis Workshop)
More about Sound Analysis Workshops
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