Convolutional Neural Networks for 1-D Many-Channel Data
Technical Report,01 Sep 2017,31 May 2018
US Army Research Laboratory Aberdeen Proving Ground United States
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Deep convolutional neural networks CNNs represent the state of the art in image recognition. The same properties that led to their success in that domain allow them to be applied to superficially very different problems with minimal modification. In this work, we have modified a simple CNN, originally written to classify digits in the MNIST database 28 28 pixels, 1 channel, for use on 1-D acoustic data taken from experiments focused on crack detection 8,000 data points, 72 channels. Though the models predictive ability is limited to fitting the trend, its partial success suggests that the application of convolutional networks to novel domains deserves further attention.