J Acoust Soc Am 100(2):1209–1219īardeli R, Wolff D, Kurth F, Koch M, Tauchert K-H, Frommolt K-H (2010) Detecting bird sounds in a complex acoustic environment and application to bioacoustic monitoring. This process is experimental and the keywords may be updated as the learning algorithm improves.Īnderson SE, Dave AS, Margoliash D (1996) Template-based automatic recognition of birdsong syllables from continuous recordings. These keywords were added by machine and not by the authors. The popularity of automated digital field recording devices allows storing a great amount of data that urgently requires an easy browsing approach to consolidate sound computing. These metrics capture the emerging patterns of a sonic environment and allow characterizing the soundscape in delimited areas of interest. Spectral analysis, which is central in bioacoustics research, offers some metrics to evaluate the complexity of the sonic environment: acoustic entropy index, median of amplitude envelope, acoustic richness, acoustic dissimilarity index, acoustic complexity index. Single microphones, sets of adjacent microphones, and regular arrays of microphones are some possibilities for collecting sound data from the environment. Frequency, pitch, period, wavelength, sound speed, wavenumber, amplitude, sound pressure, sound power, sound intensity, and loudness are some of the distinctive parameters used to describe a sound.Ī sound pressure measurement and a spectral-frequency analysis are the two distinct approaches adopted today to collect information on the sonic environment.įield recording now offers a great variety of recording devices that can sample at different rates and in different types of digital memories. For this reason, many descriptors are required to approach the behavior of sound and its effects on physical and biological objects. Sound is a complex phenomenon that copies the environmental characteristics of the context in which is generated.
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