Detection and Classification Methods for Animal Sounds Article Swipe
Julie N. Oswald
,
Christine Erbe
,
William L. Gannon
,
Shyam Madhusudhana
,
Jeànette A. Thomas
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-97540-1_8
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/978-3-030-97540-1_8
Classification of the acoustic repertoires of animals into sound types is a useful tool for taxonomic studies, behavioral studies, and for documenting the occurrence of animals. Classification of acoustic repertoires enables the identification of species, age, gender, and individual identity, correlations between sound types and behavior, the identification of changes in vocal behavior over time or in response to anthropogenic noise, comparisons between the repertoires of populations living in different geographic regions and environments, and the development of software tools for automated signal processing. Techniques for classification have evolved over time as technical capabilities have expanded. Initially, researchers applied qualitative methods, such as listening and visually discerning sounds in spectrograms. Advances in computer technology and the development of software for the automatic detection and classification of sounds have allowed bioacousticians to quickly find sounds in recordings, thus significantly reducing analysis time and enabling the analysis of larger datasets. In this chapter, we present software algorithms for automated signal detection (based on energy, Teager–Kaiser energy, spectral entropy, matched filtering, and spectrogram cross-correlation) as well as for signal classification (e.g., parametric clustering, principal component analysis, discriminant function analysis, classification trees, artificial neural networks, random forests, Gaussian mixture models, support vector machines, dynamic time-warping, and hidden Markov models). Methods for evaluating the performance of automated tools are presented (i.e., receiver operating characteristics and precision-recall) and challenges with classifying animal sounds are discussed.
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Metadata
- Type
- book-chapter
- Language
- en
- Landing Page
- https://doi.org/10.1007/978-3-030-97540-1_8
- https://link.springer.com/content/pdf/10.1007/978-3-030-97540-1_8.pdf
- OA Status
- gold
- Cited By
- 19
- References
- 238
- Related Works
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- OpenAlex ID
- https://openalex.org/W4312742493
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4312742493Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/978-3-030-97540-1_8Digital Object Identifier
- Title
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Detection and Classification Methods for Animal SoundsWork title
- Type
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book-chapterOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
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Julie N. Oswald, Christine Erbe, William L. Gannon, Shyam Madhusudhana, Jeànette A. ThomasList of authors in order
- Landing page
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https://doi.org/10.1007/978-3-030-97540-1_8Publisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/978-3-030-97540-1_8.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/978-3-030-97540-1_8.pdfDirect OA link when available
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Spectrogram, Computer science, Hidden Markov model, Pattern recognition (psychology), Artificial intelligence, Identification (biology), Speech recognition, Software, Mixture model, Dynamic time warping, Artificial neural network, Machine learning, Biology, Botany, Programming languageTop concepts (fields/topics) attached by OpenAlex
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19Total citation count in OpenAlex
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2025: 6, 2024: 8, 2023: 5Per-year citation counts (last 5 years)
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238Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 3, 23, 32, 47, 64, 76, 116, 121, 144, 209 |
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| abstract_inverted_index.animals. | 26 |
| abstract_inverted_index.behavior | 53 |
| abstract_inverted_index.chapter, | 151 |
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