Separating overlapping bat calls with a bi-directional long short-term memory network Article Swipe
Kangkang Zhang
,
Tong Liu
,
Shengjing Song
,
Xin Zhao
,
Shijun Sun
,
Walter Metzner
,
Jiang Feng
,
Ying Liu
·
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/2019.12.15.876656
YOU?
·
· 2019
· Open Access
·
· DOI: https://doi.org/10.1101/2019.12.15.876656
Acquiring clear and usable audio recordings is critical for acoustic analysis of animal vocalizations. Bioacoustics studies commonly face the problem of overlapping signals, but the issue is often ignored, as there is currently no satisfactory solution. This study presents a bi-directional long short-term memory (BLSTM) network to separate overlapping bat calls and reconstruct waveform audio sounds. The separation quality was evaluated using seven temporal-spectrum parameters. The applicability of this method for bat calls was assessed using six different species. In addition, clustering analysis was conducted with separated echolocation calls from each population. Results showed that all syllables in the overlapping calls were separated with high robustness across species. A comparison between the seven temporal-spectrum parameters showed no significant difference and negligible deviation between the extracted and original calls, indicating high separation quality. Clustering analysis of the separated echolocation calls also produced an accuracy of 93.8%, suggesting the reconstructed waveform sounds could be reliably used. These results suggest the proposed technique is a convenient and automated approach for separating overlapping calls using a BLSTM network. This powerful deep neural network approach has the potential to solve complex problems in bioacoustics. Author summary In recent years, the development of recording techniques and devices in animal acoustic experiment and population monitoring has led to a sharp increase in the volume of sound data. However, the collected sound would be overlapped because of the existence of multiple individuals, which laid restrictions on taking full advantage of experiment data. Besides, more convenient and automatic methods are needed to cope with the large datasets in animal acoustics. The echolocation calls and communication calls of bats are variable and often overlapped with each other both in the recordings from field and laboratory, which provides an excellent template for research on animal sound separation. Here, we firstly solved the problem of overlapping calls in bats successfully based on deep neural network. We built a network to separate the overlapping calls of six bat species. All the syllables in overlapping calls were separated and we found no significant difference between the separated syllables with non-overlapping syllables. We also demonstrated an instance of applying our method on species classification. Our study provides a useful and efficient model for sound data processing in acoustic research and the proposed method has the potential to be generalized to other animal species.
Related Topics
Concepts
Human echolocation
Computer science
Bioacoustics
Speech recognition
USable
Waveform
Population
Cluster analysis
Artificial neural network
Sound recording and reproduction
Artificial intelligence
Pattern recognition (psychology)
Term (time)
Robustness (evolution)
Acoustics
Biology
Telecommunications
Gene
Quantum mechanics
Demography
Sociology
Physics
Radar
World Wide Web
Biochemistry
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2019.12.15.876656
- https://www.biorxiv.org/content/biorxiv/early/2019/12/15/2019.12.15.876656.full.pdf
- OA Status
- green
- Cited By
- 3
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2994892785
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W2994892785Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2019.12.15.876656Digital Object Identifier
- Title
-
Separating overlapping bat calls with a bi-directional long short-term memory networkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2019Year of publication
- Publication date
-
2019-12-15Full publication date if available
- Authors
-
Kangkang Zhang, Tong Liu, Shengjing Song, Xin Zhao, Shijun Sun, Walter Metzner, Jiang Feng, Ying LiuList of authors in order
- Landing page
-
https://doi.org/10.1101/2019.12.15.876656Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2019/12/15/2019.12.15.876656.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2019/12/15/2019.12.15.876656.full.pdfDirect OA link when available
- Concepts
-
Human echolocation, Computer science, Bioacoustics, Speech recognition, USable, Waveform, Population, Cluster analysis, Artificial neural network, Sound recording and reproduction, Artificial intelligence, Pattern recognition (psychology), Term (time), Robustness (evolution), Acoustics, Biology, Telecommunications, Gene, Quantum mechanics, Demography, Sociology, Physics, Radar, World Wide Web, BiochemistryTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1, 2021: 2Per-year citation counts (last 5 years)
- References (count)
-
35Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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