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About Bioacoustic Detection With Wavelet-Conditioned Convolutional Neural Networks
This document discusses using convolutional neural networks (CNNs) with wavelet transformations of audio recordings to detect mosquitoes and classify bird species from acoustic data. The key findings are: 1) CNNs operating on wavelet transformations of audio demonstrate superior performance over classifiers using handcrafted features for acoustic event detection. 2) Wavelet transformations provide a clear benefit over the commonly used short-time Fourier transform. 3) Features handcrafted for a specific dataset do not generalize well, while CNNs trained on generic features achieve comparable results across datasets.
Detailed Information
| Author: | ['Numan Abdullah'] |
|---|---|
| Publication Year: | 2020 |
| Pages: | 13 |
| Language: | English |
| Format: | |
| Price: | FREE |
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