Threshold Estimation in Energy-Based Methods for Segmenting Birdsong Recordings

Monitoring wildlife populations is important to assess ecosystem health, attend environmental protection activities and undertake research studies about ecology. However, the traditional techniques are temporally and spatially limited; in order to extract information quickly and accurately about the current state of the environment, processing and recognition of acoustic signals are used. In the literature, several research studies about automatic classification of species through their vocalizations are found; however, in many of them the segmentation carried out in the preprocessing stage is briefly mentioned and, therefore, it is difficult to be reproduced by other researchers. This paper is specifically focused on detection of regions of interest in the audio recordings. A methodology for threshold estimation in segmentation techniques based on energy of a frequency band of a birdsong recording is described. Experiments were carried out using chunks taken from the RMBL-Robin database; results showed that a good performance of segmentation can be obtained by computing a threshold as a linear function where the independent variable is the estimated noise.