Babbling bats and raucous reefs Gottesman, Benjamin
Science (American Association for the Advancement of Science),
10/2022, Letnik:
378, Številka:
6617
Journal Article
Recenzirano
Bioacoustics can aid in understanding and conserving species and ecosystems
Variation in avian diversity in space and time is commonly used as a metric to assess environmental changes. Conventionally, such data were collected by expert observers, but passively collected ...acoustic data is rapidly emerging as an alternative survey technique. However, efficiently extracting accurate species richness data from large audio datasets has proven challenging. Recent advances in deep artificial neural networks (DNNs) have transformed the field of machine learning, frequently outperforming traditional signal processing techniques in the domain of acoustic event detection and classification. We developed a DNN, called BirdNET, capable of identifying 984 North American and European bird species by sound. Our task-specific model architecture was derived from the family of residual networks (ResNets), consisted of 157 layers with more than 27 million parameters, and was trained using extensive data pre-processing, augmentation, and mixup. We tested the model against three independent datasets: (a) 22,960 single-species recordings; (b) 286 h of fully annotated soundscape data collected by an array of autonomous recording units in a design analogous to what researchers might use to measure avian diversity in a field setting; and (c) 33,670 h of soundscape data from a single high-quality omnidirectional microphone deployed near four eBird hotspots frequented by expert birders. We found that domain-specific data augmentation is key to build models that are robust against high ambient noise levels and can cope with overlapping vocalizations. Task-specific model designs and training regimes for audio event recognition perform on-par with very complex architectures used in other domains (e.g., object detection in images). We also found that high temporal resolution of input spectrograms (short FFT window length) improves the classification performance for bird sounds. In summary, BirdNET achieved a mean average precision of 0.791 for single-species recordings, a F0.5 score of 0.414 for annotated soundscapes, and an average correlation of 0.251 with hotspot observation across 121 species and 4 years of audio data. By enabling the efficient extraction of the vocalizations of many hundreds of bird species from potentially vast amounts of audio data, BirdNET and similar tools have the potential to add tremendous value to existing and future passively collected audio datasets and may transform the field of avian ecology and conservation.
•We developed a DNN, called BirdNET, capable of identifying 984 North American and European bird species by sound•We tested the model against three independent datasets of single-species recordings and fully annotated soundscape data•BirdNET achieved a mean average precision of 0.791 for single-species recordings and a F0.5 score of 0.414 for annotated soundscapes•Our system is publicly available and can be used by other researchers•We also provide online demos and prototypes on out project website at https://birdnet.cornell.edu
In this study, we describe the calls emitted by Dendropsophus microps, a species of frog in the family Hylidae, in Serra da Mantiqueira, Campos do Jordão, São Paulo, Brazil, and evaluate their ...functions in the social context using playback experiments. Between October 2016 and November 2017, 15 males of D. microps were recorded. Six hundred and five calls were analyzed and the existence of five types of call compositions was verified: simple call “A normal”; compound call “Af”; shorter “A” call + longer “A” call; “A + Af” calls; and simple call “B”; demonstrating the presence of compound calling in the vocalization structure. One thousand, six hundred and seventeen calls were recorded during the playback experiment in response to the 5 previously constructed stimuli. The “B” call was the most emitted in response to stimuli. The “A”, “Af”, “AA” and “AAf” stimuli caused visual signaling. We recorded a series of 4 to 5 “A” calls during agonistic interactions. Call "B" was identified as an advertisement call and calls "A", "Af", "AA" and "AAf" as aggressive calls. The presence of visual communication in addition to the acoustic communication of these individuals was also observed. The findings increase knowledge of the calling repertoire of this species and can be used in the future in other behavioral and taxonomic studies.
India has been stated to have 10% of the world's total bat's diversity. The present survey was aimed to study the bat species diversity, distribution and activity patterns in north-western Himalayan ...region of India. Field surveys were conducted and echolocation calls were recorded using bat detector, Echometer touch 2 in Shiwalik ranges of Himalaya. The Greater False Vampire Bat (Megaderma lyra) has a broad distribution range that stretches from south to south east Asia. In India the distribution of the species was mostly recorded from southern subtropical coastal regions and north-eastern subtropical moist evergreen forests. In the present study The Greater False Vampire Bat (Megaderma lyra) has been recorded for the first time from Garhwal region of north-western Himalayas. Greater False Vampire Bat (Megaderma lyra) having long forearm (>6.63 ± 0.03cm) and lengthy ear (>3.66 ± 0.08cm), which is a distinguishable feature of the family Megadermatidae. The peak frequency (FMaxE) of echolocation was recorded as 50.295 ± 9.18 kHz. This frequency was initiated at 108.20 ± 2.51 kHz and terminated at 30.76 ± 1.37 kHz. The call structure recorded was very distinguishable and specific to this family.
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