Abstract
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.
The following individuals from Jasco Applied Sciences are thanked for their continued support of this project: Bruce Martin, Katie Kowarski, and Briand Gaudet. Additional thanks to Stan Matwin from Dalhousie University. Collaboration between researchers at Jasco Applied Sciences and Dalhousie University was made possible through an NSERC Engage Grant.
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References
Abdel-Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., Yu, D.: Convolutional neural networks for speech recognition. IEEE/ACM Trans. Audio Speech Lang. Process. 22(10), 1533–1545 (2014)
Choi, K., Fazekas, G., Sandler, M., Cho, K.: Convolutional recurrent neural networks for music classification. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2392–2396. IEEE (2017)
Deng, L., et al.: Recent advances in deep learning for speech research at Microsoft. In: ICASSP, vol. 26, p. 64 (2013)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Humphrey, E.J., Bello, J.P.: Rethinking automatic chord recognition with convolutional neural networks. In: 2012 11th International Conference on Machine Learning and Applications (ICMLA), vol. 2, pp. 357–362. IEEE (2012)
Kingma, D.P., Ba, J.L.: Adam: a method for stochastic optimization. In: Proceedings of the 3rd International Conference on Learning Representations (2014)
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Thomas, M. (2019). Towards a Novel Data Representation for Classifying Acoustic Signals. In: Meurs, MJ., Rudzicz, F. (eds) Advances in Artificial Intelligence. Canadian AI 2019. Lecture Notes in Computer Science(), vol 11489. Springer, Cham. https://doi.org/10.1007/978-3-030-18305-9_67
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DOI: https://doi.org/10.1007/978-3-030-18305-9_67
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