Tag: deep neural networks

As the size and number of acoustic datasets increase, accurately and quickly matching the bioacoustics signals to their corresponding sources becomes more challenging and important. This is especially difficult in noisy, natural acoustic environments. At the 182nd ASA Meeting, Elizabeth Ferguson, from Ocean Science Analytics, will describe how DeepSqueak, a deep learning tool, can classify underwater acoustic signals. It uses deep neural network image recognition and classification methods to determine the important features within spectrograms, then match those features to specific sources.