Underwater Acoustic Detection and Classification for Cetaceans’ Vocalizations
Date Issued
2011
Date
2011
Author(s)
Fang, Yin-Ying
Abstract
Passive acoustics has been well recognized as an important tool for observing marine animals and long-term underwater environment monitoring. Since the amount of data is enormous, it is needed to have an effective auto-detector to select critical features and classify their patterns from the recorded acoustic signal. In this study, we had developed an automatic detector with both the feature extraction and classification modules. In the feature extraction module, we select features from the entropy and end-point of the time signal. Then, we normalized the extracted features as inputs for the classification module based on the theory of back propagation neural network (BPNN). The BPNN will be trained and tested using the cetaceans’ acoustic signals from database of Cornell University Macaulay Library Marine Collection until the network becomes stable and convergent. The selected samples are commonly found cetaceans from the northeastern offshore of Taiwan and Guishan Island.
Currently, our detector had obtained fairly good recognition rate for classifying cetaceans. In the future, our automatic detector can be applied to classify similar acoustic signals if we can improve the accuracy. We believe the proposed automatic detector will be a robust tool, which supersedes the experienced human operators due to less time consuming and low labor cost.
Subjects
Entropy detector
End-point detecting
Neural Network
Back Propagation
SDGs
Type
thesis
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