Automatic recognition of bird individuals on an open set using as-is recordings

Ptáček, Ladislav; Machlica, Lukáš; Linhart, Pavel; Jaška, Pavel; Müller, Luděk

doi:10.1080/09524622.2015.1089524

Cited by 32 publications

(22 citation statements)

References 29 publications

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“…However, birds might have some other features associated with 'voice quality'. There are several studies, including our previous study on Chiffchaffs, that proved that methods used for content-independent speaker identification in humans can be adapted to identify bird individuals (Fox 2008;Cheng et al 2010;Ptáček et al 2016). However, it is still necessary to prove that re-identification with these methods is possible on a much larger timescale.…”

Section: Other Possible Cues To Identity In Chiffchaff Songsmentioning

confidence: 99%

Cues to individual identity in songs of songbirds: testing general song characteristics in Chiffchaffs Phylloscopus collybita

2017

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Individual variation in vocalizations has been widely studied among different animal taxa, and it is commonly reported that vocalizations could be potentially used to monitor individuals in many species. Songbirds represent a challenging group of animals for the study of signalling of individual identity. They are highly vocal, but their songs are complex and can change over time. In this study, we tested whether general song characteristics, which are independent of song type, can be used to discriminate and consistently identify Chiffchaff males within and between days and between years. There was individual variation in songs of recorded Chiffchaffs, and it was possible to easily discriminate between males at any one point in time. However, the level of re-identification of males across days and years was low. For effective identification it was necessary to compare songs of a single song type. However, Chiffchaffs haphazardly switch among song types, sometimes singing the same song type for a long time, making it difficult to collect equivalent song types or to sample the birds' full repertoires. For example, 5-min recordings of males taken in different years did not contain equivalent song types, leading to low identification success. Although we were not successful in the re-identification of males based on general song characteristics, we discuss methods of acoustic identification which are not dependent on song repertoire content and are potentially valuable tools for the study of species such as the Chiffchaff.

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Section: Other Possible Cues To Identity In Chiffchaff Songsmentioning

confidence: 99%

Cues to individual identity in songs of songbirds: testing general song characteristics in Chiffchaffs Phylloscopus collybita

2017

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“…For further discussion of the challenges presented by this type of open-set problem (i.e. where the distribution of classes in the test data is not known a-priori) in the context of bioacoustics see [47]. …”

Section: Methodsmentioning

confidence: 99%

Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring

et al. 2016

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Automatic classification of animal vocalizations has great potential to enhance the monitoring of species movements and behaviors. This is particularly true for monitoring nocturnal bird migration, where automated classification of migrants’ flight calls could yield new biological insights and conservation applications for birds that vocalize during migration. In this paper we investigate the automatic classification of bird species from flight calls, and in particular the relationship between two different problem formulations commonly found in the literature: classifying a short clip containing one of a fixed set of known species (N-class problem) and the continuous monitoring problem, the latter of which is relevant to migration monitoring. We implemented a state-of-the-art audio classification model based on unsupervised feature learning and evaluated it on three novel datasets, one for studying the N-class problem including over 5000 flight calls from 43 different species, and two realistic datasets for studying the monitoring scenario comprising hundreds of thousands of audio clips that were compiled by means of remote acoustic sensors deployed in the field during two migration seasons. We show that the model achieves high accuracy when classifying a clip to one of N known species, even for a large number of species. In contrast, the model does not perform as well in the continuous monitoring case. Through a detailed error analysis (that included full expert review of false positives and negatives) we show the model is confounded by varying background noise conditions and previously unseen vocalizations. We also show that the model needs to be parameterized and benchmarked differently for the continuous monitoring scenario. Finally, we show that despite the reduced performance, given the right conditions the model can still characterize the migration pattern of a specific species. The paper concludes with directions for future research.

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“…Many bird species exhibit geographical variations on their songs (Hill et al 2013). Although this phenomenon is a challenge in species recognition, it could possibly allow individual recognition (Gilbert et al 1994, Cheng et al 2010, Baldo and Mennill 2011, Dent and Molles 2016, Ptacek et al 2016. 5.…”

Section: Preprocessing Call Detection and Segmentationmentioning

confidence: 99%

Automated birdsong recognition in complex acoustic environments: a review

Priyadarshani

Marsland

Castro

2018

Journal of Avian Biology

209

216

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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.

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Automatic recognition of bird individuals on an open set using as-is recordings

Cited by 32 publications

References 29 publications

Cues to individual identity in songs of songbirds: testing general song characteristics in Chiffchaffs Phylloscopus collybita

Cues to individual identity in songs of songbirds: testing general song characteristics in Chiffchaffs Phylloscopus collybita

Towards the Automatic Classification of Avian Flight Calls for Bioacoustic Monitoring

Automated birdsong recognition in complex acoustic environments: a review

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