01900nas a2200193   4500008004100000022001400041245008000055210006900135260001600204300001600220490000800236520131000244100001901554700002101573700001901594700002301613700001901636856005101655       2018                        eng d  a0001-496600aTowards the topology of autoencoder of calls versus clicks of marine mammal0 aTowards the topology of autoencoder of calls versus clicks of ma  cJan-09-2018  a1777 - 17780 v1443 a<p>The goal is to learn the features and the representation adapted for cetacean sound dynamics without any priors. Thus, we develop data driven model to generate voicing and click of cetaceans audio signals. We learn representation and features of stationary or nonstationary emission using neural network from raw audio. We use different types of convolutions (causal, with strides, with dilation [1]), or gradient inversion [2]. Experiments are conducted on various kind of calls of humpback whales from nips4b challenge [3] or Orca whale. We compare the topology for transient encoding on Physeters and Inia g. For each model, we detail the resulting filters and discuss on the topology. We acknowledge Region PACA and NortekMED for Roger&rsquo;s Phd grant, &amp; DGA and R&eacute;gion Haut de France for Ferrari&rsquo;s Phd grant. [1] Oord, Dieleman, Zen, Simonyan, Vinyals, Graves et al. Wavenet : A generative model for raw audio, arXiv:1609.03499, 2016 [2] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv:1711.04313, 2017 [3] Glotin, LeCun, Mallat et al. Proc. 1st wkp on Neural Information Processing for Bioacoustics NIPS4B, joint to NIPS Alberta USA, 2013 http://sabiod.org/nips4b/challenge2.html, http://sabiod.org/NIPS4B2013_book.pdf</p>
1 aRoger, Vincent1 aFerrari, Maxence1 aMarxer, Ricard1 aChamroukhi, Faicel1 aGlotin, Hervé  uhttp://asa.scitation.org/doi/10.1121/1.5067859