Computational Bioacoustic Scene Analysis

Stowell, Dan

doi:10.1007/978-3-319-63450-0_11

Cited by 32 publications

(20 citation statements)

References 83 publications

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“…SS in an overdetermined system will also improve the monitoring of soundscape dynamics. While more acoustic sensors are deployed for longer durations to investigate global and regional soundscape changes, soundscape‐based ecosystem monitoring nevertheless runs into a problem of scalability (Stowell ). As the volume of data grows, with the expectation of real‐time processing, in conservation management, the development of efficient frameworks of SS will be critical.…”

Section: A Roadmap To Effective Retrieval Of Soundscape Informationmentioning

confidence: 99%

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Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Lin

Tsao

2019

Remote Sens Ecol Conserv

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A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source-specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model-based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model-based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model-based techniques can yield state-of-the-art separation performance, accordingly facilitate content-based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high-performance source separation for ecoacoustics, and accordingly improve soundscape-based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross-disciplinary collaboration. (biophony), their habitats (geophony) and human development (anthropophony) (Dumyahn and Pijanowski 2011; Pijanowski et al. 2011; Sueur and Farina 2015; Krause and Farina 2016). By listening to biotic sounds, the composition of soniferous animals can be characterized and applied to assess wildlife biodiversity (Andr e et al. 2011; Saito et al. 2015; Lin et al. 2017a; Ross et al. 2018). On the other hand, abiotic sounds can be used to evaluate the ecosystem dynamics associated with

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Section: A Roadmap To Effective Retrieval Of Soundscape Informationmentioning

confidence: 99%

“…; Xie et al. ; Stowell ). Some features are reliable for investigating activity patterns of one or a few species, but there is no guarantee that they can be applied for non‐target species or abiotic sounds.…”

Section: Introductionmentioning

confidence: 99%

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Lin

Tsao

2019

Remote Sens Ecol Conserv

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“…Tags with many gigabytes of storage already exist, so whether the factor limiting recording duration is battery or storage will depend on the capacity of the battery as well as the audio-recording duty cycle and sampling rate. To this end, new research efforts have focused on segmentation and classification algorithms that automatically detect events of interest (Salamon et al, 2016;Stowell, 2018;Stowell et al, 2017;Towsey et al, 2018). The marine bio-acoustics community has been dealing with this challenge for many years because of their immense amount of recordings and they have developed some useful strategies (Johnson, 2014;Johnson et al, 2009).…”

Section: A Few Technical Notes About On-board Sound Recordingsmentioning

confidence: 99%

Using on-board sound recordings to infer behaviour of free-moving wild animals

Greif

Yovel

2019

Journal of Experimental Biology

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Technological advances in the last 20 years have enabled researchers to develop increasingly sophisticated miniature devices (tags) that record an animal's behaviour not from an observational, external viewpoint, but directly on the animals themselves. So far, behavioural research with these tags has mostly been conducted using movement or acceleration data. But on-board audio recordings have become more and more common following pioneering work in marine mammal research. The first questions that come to mind when recording sound on-board animals concern their vocal behaviour. When are they calling? How do they adjust their behaviour? What acoustic parameters do they change and how? However, other topics like foraging behaviour, social interactions or environmental acoustics can now be addressed as well and offer detailed insight into the animals' daily life. In this Review, we discuss the possibilities, advantages and limitations of on-board acoustic recordings. We focus primarily on bats as their active-sensing, echolocating lifestyle allows many approaches to a multi-faceted acoustic assessment of their behaviour. The general ideas and concepts, however, are applicable to many animals and hopefully will demonstrate the versatility of on-board acoustic recordings and stimulate new research.

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“…More generally, computational methods for various automatic recognition tasks have recently been dominated and dramatically improved by new trends in machine learning, including deep learning. In bioacoustic tasks, machine learning has enabled extremely strong performance in automatic detection of animal sounds [43], automatic species classification from sound [44,45] and other tasks [46].…”

Section: Introductionmentioning

confidence: 99%

Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions

Stowell

Petrusková

Šálek

et al. 2019

J. R. Soc. Interface.

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Many animals emit vocal sounds which, independently from the sounds’ function, contain some individually distinctive signature. Thus the automatic recognition of individuals by sound is a potentially powerful tool for zoology and ecology research and practical monitoring. Here, we present a general automatic identification method that can work across multiple animal species with various levels of complexity in their communication systems. We further introduce new analysis techniques based on dataset manipulations that can evaluate the robustness and generality of a classifier. By using these techniques, we confirmed the presence of experimental confounds in situations resembling those from past studies. We introduce data manipulations that can reduce the impact of these confounds, compatible with any classifier. We suggest that assessment of confounds should become a standard part of future studies to ensure they do not report over-optimistic results. We provide annotated recordings used for analyses along with this study and we call for dataset sharing to be a common practice to enhance the development of methods and comparisons of results.

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Computational Bioacoustic Scene Analysis

Cited by 32 publications

References 83 publications

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Source separation in ecoacoustics: a roadmap towards versatile soundscape information retrieval

Using on-board sound recordings to infer behaviour of free-moving wild animals

Automatic acoustic identification of individuals in multiple species: improving identification across recording conditions

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