Classification of animal sounds in a hyperdiverse rainforest using
Convolutional Neural Networks
- URL: http://arxiv.org/abs/2111.14971v1
- Date: Mon, 29 Nov 2021 21:34:57 GMT
- Title: Classification of animal sounds in a hyperdiverse rainforest using
Convolutional Neural Networks
- Authors: Yuren Sun, Tatiana Midori Maeda, Claudia Solis-Lemus, Daniel
Pimentel-Alarcon, Zuzana Burivalova
- Abstract summary: Automated species detection from passively recorded soundscapes via machine-learning approaches is a promising technique.
We use soundscapes from a tropical forest in Borneo and a Convolutional Neural Network model (CNN) created with transfer learning.
Our results suggest that transfer learning and data augmentation can make the use of CNNs to classify species' vocalizations feasible even for small soundscape-based projects with many rare species.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: To protect tropical forest biodiversity, we need to be able to detect it
reliably, cheaply, and at scale. Automated species detection from passively
recorded soundscapes via machine-learning approaches is a promising technique
towards this goal, but it is constrained by the necessity of large training
data sets. Using soundscapes from a tropical forest in Borneo and a
Convolutional Neural Network model (CNN) created with transfer learning, we
investigate i) the minimum viable training data set size for accurate
prediction of call types ('sonotypes'), and ii) the extent to which data
augmentation can overcome the issue of small training data sets. We found that
even relatively high sample sizes (> 80 per call type) lead to mediocre
accuracy, which however improves significantly with data augmentation,
including at extremely small sample sizes, regardless of taxonomic group or
call characteristics. Our results suggest that transfer learning and data
augmentation can make the use of CNNs to classify species' vocalizations
feasible even for small soundscape-based projects with many rare species. Our
open-source method has the potential to enable conservation initiatives become
more evidence-based by using soundscape data in the adaptive management of
biodiversity.
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