Towards a Novel Data Representation for Classifying Acoustic Signals

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11489)


In this paper, we evaluate a novel data representation of acoustic signals that builds upon the traditional spectrogram representation through interpolation. The novel representation is used in training a deep Convolutional Neural Network for the task of marine mammal species classification. The resulting classifier is compared in terms of performance to several other classifiers trained on traditional spectrograms.


Deep learning Convolutional Neural Networks Classification Signal processing Bioacoustics 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Dalhousie University Faculty of Computer ScienceHalifaxCanada

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