Conservationists are increasingly using autonomous acoustic recorders to determine the presence/absence and the abundance of bird species. Unlike humans, these recorders can be left in the field for ...extensive periods of time in any habitat. Although data acquisition is automated, manual processing of recordings is labour intensive, tedious, and prone to bias due to observer variations. Hence automated birdsong recognition is an efficient alternative.
However, only few ecologists and conservationists utilise the existing birdsong recognisers to process unattended field recordings because the software calibration time is exceptionally high and requires considerable knowledge in signal processing and underlying systems, making the tools less user‐friendly. Even allowing for these difficulties, getting accurate results is exceedingly hard. In this review we examine the state‐of‐the‐art, summarising and discussing the methods currently available for each of the essential parts of a birdsong recogniser, and also available software. The key reasons behind poor automated recognition are that field recordings are very noisy, calls from birds that are a long way from the recorder can be faint or corrupted, and there are overlapping calls from many different birds. In addition, there can be large numbers of different species calling in one recording, and therefore the method has to scale to large numbers of species, or at least avoid misclassifying another species as one of particular interest. We found that these areas of importance, particularly the question of noise reduction, are amongst the least researched. In cases where accurate recognition of individual species is essential, such as in conservation work, we suggest that specialised (species‐specific) methods of passive acoustic monitoring are required. We also believe that it is important that comparable measures, and datasets, are used to enable methods to be compared.
In precision livestock farming, studies show that bird sound can be employed as a biomarker of health condition. One of the most important steps for this purpose is to study the feasibility of using ...acoustic features as criteria for disease diagnosis. In this research five acoustic features of bird calls were evaluated for determination of bird health condition. Signals were collected from broilers grown in three groups: control, challenged with Bronchitis, and challenged with Newcastle disease. Results of data analysis showed that, among the 5 acoustic features studied, wavelet entropy (WET) had the best performance and was able to detect Bronchitis on the third day after inoculation with 83% accuracy while the type II error in this test (incorrectly detecting sick bird as healthy) was less than 14% and 6% on the third day and fourth day, respectively. In the case of Newcastle disease, although WET and Mel cepstral coefficients (MFCC) exhibited similar accuracy (80% and 78% respectively on the fourth day), but the difference was that WET was more reliable in detecting healthy birds while MFCC had better performance detecting challenged birds.
Plants can talk: a new era in plant acoustics Hussain, Muzammil; Khashi u Rahman, Muhammad; Mishra, Ratnesh Chandra ...
Trends in plant science,
09/2023, Letnik:
28, Številka:
9
Journal Article
Recenzirano
Plants release chemical signals to interact with their environment when exposed to stress. Khait and colleagues unveiled that plants ‘verbalize’ stress by emitting airborne sounds. These can train ...machine learning models to identify plant stressors. This unlocks a new path in plant-environment interactions research with multiple possibilities for future applications.
Plants release chemical signals to interact with their environment when exposed to stress. Khait and colleagues unveiled that plants ‘verbalize’ stress by emitting airborne sounds. These can train machine learning models to identify plant stressors. This unlocks a new path in plant-environment interactions research with multiple possibilities for future applications.
Automated acoustic recorders can collect long-term soundscape data containing species-specific signals in remote environments. Ecologists have increasingly used them for studying diverse fauna around ...the globe. Deep learning methods have gained recent attention for automating the process of species identification in soundscape recordings. We present an end-to-end pipeline for training a convolutional neural network (CNN) for multi-species multi-label classification of soundscape recordings, starting from raw, unlabeled audio. Training data for species-specific signals are collected using a semi-automated procedure consisting of an efficient template-based signal detection algorithm and a graphical user interface for rapid detection validation. A CNN is then trained based on mel-spectrograms of sound to predict the set of species present in a recording. Transfer learning of a pre-trained model is employed to reduce the necessary training data and time. Furthermore, we define a loss function that allows for using true and false template-based detections to train a multi-class multi-label audio classifier. This approach leverages relevant absence (negative) information in training, and reduces the effort in creating multi-label training data by allowing weak labels. We evaluated the pipeline using a set of soundscape recordings collected across 749 sites in Puerto Rico. A CNN model was trained to identify 24 regional species of birds and frogs. The semi-automated training data collection process greatly reduced the manual effort required for training. The model was evaluated on an excluded set of 1000 randomly sampled 1-min soundscapes from 17 sites in the El Yunque National Forest. The test recordings contained an average of ~3 present target species per recording, and a maximum of 8. The test set also showed a large class imbalance with most species being present in less than 5% of recordings, and others present in >25%. The model achieved a mean-average-precision of 0.893 across the 24 species. Across all predictions, the total average-precision was 0.975.
•A multi-label CNN was trained for classification of bird and frog vocalizations in soundscape recordings.•Training data was collected using a template-based sound detector and a graphical user interface.•Multi-label learning was achieved from single-label positive and negative training data by using a custom training loss.•Using false-positive template-based detections as negative information for training yielded strong precision.
Animal communication networks consist of multisensory signals and often include an acoustic element. Many freshwater fishes, including the blacktail shiner, Cyprinella venusta, produce specific ...vocalizations during courtship and agonistic behaviours. Similar to other vocal taxa, fish communication networks are vulnerable to noise pollution. The dominant frequencies of anthropogenic noise often overlap spectrally with animal acoustic signals. To investigate the role of acoustic signalling in breeding soniferous fishes in a physiological context, we conducted a series of acoustic playback trials. We measured waterborne levels of oestradiol (E2) and prostaglandin F2α (PGF2α) in gravid females exposed to courtship calls, and levels of 11-ketotestosterone (11KT) in males exposed to agonistic calls. In a separate series of trials, we exposed individuals to calls masked with bridge traffic noise. Hormone levels were quantified using enzyme-linked immunosorbent assays (ELISA) and analysed using repeated measures ANOVAs. Gravid females exhibited a significant drop in E2 (P=0.023) and an increase in PGF2α during courtship calls (P=0.01). Our research suggests that growl signals contribute to the onset of spawning behaviours in female C. venusta and this response may be dampened by anthropogenic noise pollution.
•We examined effects of anthropogenic noise on acoustic signalling in C. venusta.•Males produce growls (courtship/spawning) and knocks (male–male aggression).•Unmasked growls modulated levels of reproductive hormones in gravid females.•Males did not respond hormonally to unimodal stimuli.•Fish exposed to masked vocalizations did not exhibit hormonal changes.
Brazil is a megadiverse country with more than 180 bat species. However, most inventories have been mostly made using mist-net sampling and roost search and due to the lack of bioacoustics studies, ...the bat fauna is certainly subrepresented and biased. The knowledge on distribution and ecology of Brazilian bats is mainly within the Phyllostomidae. Reliable data on bat echolocation calls is the key to improve the knowledge on the distribution patterns and foraging ecology of the remaining eight bat families present in the country. Our work aims to (i) integrate information on echolocation calls of non-phyllostomids occurring in Brazil; (ii) detect regional changes in the acoustic profile of those species; (iii) identify gaps in knowledge both in terms of species and regions sampled; and (iv) to point out which species are acoustically recognizable in a reliable way. Finally, we present a key to supporting the acoustic identification of non-phyllostomids in Brazil. We compiled publications on echolocation calls of Neotropical bat species occurring in Brazil and summarized qualitative and quantitative information of acoustic parameters used in call descriptions. We considered 93 non-phyllostomid bat species to occur in Brazil of which 65 have been acoustically described but for 28 we found no published information. Information on echolocation calls was retrieved from 47 publications and acquired in 17 countries. The use of bioacoustics can be a fundamental tool to expand the knowledge on Brazilian bats and improve their conservation.
•Logging and wildfire are major disturbance factors in Mountain Ash forests.•Passive acoustic surveys are effective for detecting Yellow-bellied Gliders.•Site occupancy by Yellow-bellied Gliders was ...correlated to undisturbed forest area.•Maintaining mature forest refuges will be critical for Yellow-bellied Gliders.
Habitat loss and degradation are two of the greatest threats to biodiversity conservation globally. In Australia, the incidence of wildfire and native forest logging contribute substantially to these processes and have been linked to reduced species diversity and abundance. Arboreal species such as the Yellow-bellied Glider (Petaurus australis), are thought to be susceptible to these disturbances due to their reliance on large patches of forest and hollow-bearing trees. We aimed to assess the impact of logging and wildfire on site occupancy by the Yellow-bellied Glider in Mountain Ash forests of the Central Highlands of Victoria. We deployed autonomous recording units for 14 nights at 70 sites and searched recordings for Yellow-bellied Glider vocalisations. Yellow-bellied Gliders were detected at 30% (N = 21) of sites. Site occupancy increased with an increasing proportion of the landscape (within 400 m of the recorder) that had not been logged within the last 100 years or burnt by wildfire within the last 10 years. Habitat disturbance caused by logging and fire therefore limit the site occupancy of Yellow-bellied Gliders and likely impact its conservation status. It is therefore critical that appropriate management of timber resources protects large patches of old-growth forest providing food resources and hollows, and the connectivity between large forest patches.
We report the first records of Scinax ruberoculatus Ferrão, Fraga, Moravec, Kaefer & Lima, 2018 in the state of Amapá, Brazil. We provide an updated distribution map of this ...species, comment on its occurrence in French Guiana and Suriname, and provide morphometric and bioacoustic data for a population from the municipality of Porto Grande, Amapá, including the first description of the species’ territorial call. This record from Porto Grande extends the distribution of S. ruberoculatus approximately 1430 km northeast from its type locality and helps to better understand its actual distribution.
Stressed plants show altered phenotypes, including changes in color, smell, and shape. Yet, airborne sounds emitted by stressed plants have not been investigated before. Here we show that stressed ...plants emit airborne sounds that can be recorded from a distance and classified. We recorded ultrasonic sounds emitted by tomato and tobacco plants inside an acoustic chamber, and in a greenhouse, while monitoring the plant’s physiological parameters. We developed machine learning models that succeeded in identifying the condition of the plants, including dehydration level and injury, based solely on the emitted sounds. These informative sounds may also be detectable by other organisms. This work opens avenues for understanding plants and their interactions with the environment and may have significant impact on agriculture.
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•Plants emit ultrasonic airborne sounds when stressed•The emitted sounds reveal plant type and condition•Plant sounds can be detected and interpreted in a greenhouse setting
Plants emit species- and stress-specific airborne sounds that can be detected in acoustic chambers and greenhouses.
Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For ...birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small-scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data-driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.
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