Title: Deep Networks tag the location of bird vocalisations on audio spectrograms,
Abstract: This work focuses on reliable detection and segmentation of bird
vocalizations as recorded in the open field. Acoustic detection of avian sounds
can be used for the automatized monitoring of multiple bird taxa and querying
in long-term recordings for species of interest. These tasks are tackled in
this work, by suggesting two approaches: A) First, DenseNets are applied to
weekly labeled data to infer the attention map of the dataset (i.e. Salience
and CAM). We push further this idea by directing attention maps to the YOLO v2
Deepnet-based, detection framework to localize bird vocalizations. B) A deep
autoencoder, namely the U-net, maps the audio spectrogram of bird vocalizations
to its corresponding binary mask that encircles the spectral blobs of
vocalizations while suppressing other audio sources. We focus solely on
procedures requiring minimum human attendance, suitable to scan massive volumes
of data, in order to analyze them, evaluate insights and hypotheses and
identify patterns of bird activity. Hopefully, this approach will be valuable
to researchers, conservation practitioners, and decision makers that need to
design policies on biodiversity issues.