Detection of Bat Acoustics Signals Using Voice Activity Detection Techniques with Random Forests Classification
| Autor: | Adrián Tonatiuh Ruiz, Santiago Martínez Balvanera, Everardo Robredo, Julian Equihua, Friedhelm Schwenker, Günther Palm |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
Feature engineering Voice activity detection Receiver operating characteristic Computer science business.industry Deep learning Detector Pattern recognition Statistical model Convolutional neural network Random forest 03 medical and health sciences 030104 developmental biology Artificial intelligence business |
| Zdroj: | Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications ISBN: 9783030134686 CIARP |
| DOI: | 10.1007/978-3-030-13469-3_30 |
| Popis: | Bats are indicators for ecosystem health, and therefore the determination of bat activity and species abundance provides essential information for biodiversity research and conservation monitoring. In this study, we propose a computational method for the detection of bat echolocation calls. This method uses feature engineering and consists of a statistical model-based Voice Activity Detector combined with a Random Forests classifier (VAD+RF). Using an open-access library (www.batdetective.org), we trained and tested the performance of our method and compare it to other existing detection methods. These methods include a detector based on deep neural networks along with other commercial detection systems. To visualize the detector performance over the full range of possible class distributions and misclassification costs, we calculated the Cost Curves and \(F_1\)-measure Curves. Results show that the detecting power of VAD+RF is comparable to methods based on deep learning. Based on the results we give recommendations to improve the future designs of the bat call detector. |
| Databáze: | OpenAIRE |
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