@article {57619, title = {NIPS4Bplus: a richly annotated birdsong audio dataset}, year = {2019}, abstract = {

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated recordings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of recordings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species specific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird population monitoring, species classification, birdsong vocalisation detection and classification.

}, keywords = {audio dataset, Audio signal processing, bioacoustics, Bioinformatics, Bird vocalisations, ecoacoustics, ecosystems, rich annotations}, doi = {10.7717/peerj-cs.223}, url = {https://peerj.com/articles/cs-223}, author = {Morfi, Veronica and Bas, Yves and Pamu{\l}a, Hanna and Glotin, Herv{\'e} and Stowell, Dan} } @article {53316, title = {Deep learning for ethoacoustical mapping: Application to a single Cachalot long term recording on joint observatories in Vancouver Island}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-09-2018}, pages = {1776 - 1777}, abstract = {

During February and March, 2018, a lone sperm whale known as Yukusam was recorded first by Orcalab in Johnstone Strait and subsequently on multiple hydrophones within the Salish Sea [1]. We learn and denoise these multichannel clicks trains with AutoEncoders Convolutional Neural Net (CNN). Then, we build a map of the echolocations to elucidate variations in the acoustic behavior of this unique animal over time, in different environments and distinct levels of boat noise. If CNN approximates an optimal kernel decomposition, it requires large amounts of data. Via spline functionals we offer analytics kernels with learnable coefficients do reduce it. We [1-3] identify the analytical mother wavelet to represent the input signal to directly learn the wavelet support from scratch by gradient descend on the parameters of cubic splines [2]. Supplemental material http://sabiod.org/yukusam [1] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv preprint arXiv:1711.04313, 2017 [2] Balestriero, Cosentino, Glotin, Baraniuk, WaveletNet : Spline Filters for End-to-End Deep Learning, Int. Conf. on MachineLearning, ICML, Stockholm, http://sabiod.org/bib, 2018 [3] Spong P., Symonds H., et al., Joint Observatories Following a Single male Cachalot during 12 weeks\—The Yukusam story, ASA 2018.

}, issn = {0001-4966}, doi = {10.1121/1.5067855}, url = {http://asa.scitation.org/doi/10.1121/1.5067855}, author = {Glotin, Herv{\'e} and Spong, Paul and Symonds, Helena and Roger, Vincent and Balestriero, Randall and Ferrari, Maxence and Poupard, Marion and Towers, Jared and Veirs, Scott and Marxer, Ricard and Giraudet, Pascale and James Pilkinton and Veirs, Val and Wood, Jason and Ford, John and Dakin, Thomas} } @article {53312, title = {Joint observatories following a single male Cachalot during 12 weeks {\textemdash}The Yukusam story}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-09-2018}, pages = {1958 - 1958}, abstract = {

From 11 February to 31 March 2018, a lone male sperm whale visited coastal waters from the northeast to southern ends of Vancouver Island. This whale, named \"Yukusam\" after the Namgis First Nation word for Hanson Island, near where the whale was first observed and recorded, is the first sperm whale recorded acoustically in the area since 1984 and is the only sperm whale ever observed in coastal waters between Vancouver Island and continental North America. The Yukusam tracking story is a showcase for the potential of acoustic observatory collaborations. Tracking a single animal over such time and distance is remarkable. It obviously helped that Yukusam was the only sperm whale in the area, but still, the experience hints at the potential for using diverse independent observatories collaboratively. We then aim to see all the observatories running automated detection classification and location software and having all the data tied in to a public database for a total of almost 500 Gb of recordings, with labels. Supplemental material @ http://sabiod.org/yukusam

}, issn = {0001-4966}, doi = {10.1121/1.5068557}, url = {http://asa.scitation.org/doi/10.1121/1.5068557}, author = {Spong, Paul and Symonds, Helena and Glotin, Herv{\'e} and Towers, Jared and Lisa Larsson and Dakin, Thomas and Veirs, Scott and Zwamborn, Elizabeth and James Pilkinton and Giraudet, Pascale and Veirs, Val and Wood, Jason and Ford, John} } @article {53310, title = {Towards the topology of autoencoder of calls versus clicks of marine mammal}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-09-2018}, pages = {1777 - 1778}, abstract = {

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\’s Phd grant, \& DGA and R\égion Haut de France for Ferrari\’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

}, issn = {0001-4966}, doi = {10.1121/1.5067859}, url = {http://asa.scitation.org/doi/10.1121/1.5067859}, author = {Roger, Vincent and Ferrari, Maxence and Marxer, Ricard and Chamroukhi, Faicel and Glotin, Herv{\'e}} } @article {53307, title = {The use of context in machine learning for bioacoustics}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-09-2018}, pages = {1728 - 1728}, abstract = {

Biological acoustic signals are often produced in context. Contextual information includes things such as timing between calls, conspecific or interspecies cues, and physical environmental cues such as sunrise or sunset. We show how some forms of contextual information can be used to improve the results of detection and classification tasks for biological acoustic signals. We examine how context can be used to improve labeled data, resulting in more-accurate classification results, as well as how learners can exploit context directly. We demonstrate these improvements via two bioacoustic detection/classification tasks. The first algorithm detects odontocete echolocation clicks. We used a decision support system that allowed analysts to label echolocation clicks using between call timing cues as well as other measurements and found that deep learners trained with these high quality data are able to detect clicks in adverse environments. The second algorithm applies contextual information surrounding North Atlantic right whale upcalls to improve precision and recall. [This work was supported by ONR Grant Nos. N00014-17-1-2867 and N00014-15-1-2299.]

}, issn = {0001-4966}, doi = {10.1121/1.5067665}, url = {http://asa.scitation.org/doi/10.1121/1.5067665}, author = {Roch, Marie A. and Baumann-Pickering, Simone and Cholewiak, Danielle and Fleishman, Erica and Frasier, Kaitlin E. and Glotin, Herv{\'e} and Helble, Tyler A. and John A. Hildebrand and Klinck, Holger and Lindeneau, Scott and Liu, Xiaobai and Nosal, Eva-Marie and Palmer, Kaitlin and Shiu, Yu and Singh, Gurisht} } @article {53274, title = {NIPS4Bplus: a richly annotated birdsong audio dataset}, journal = {arXiv preprint arXiv:1811.02275}, year = {2018}, abstract = {

Recent advances in birdsong detection and classification have approached a limit due to the lack of fully annotated record- ings. In this paper, we present NIPS4Bplus, the first richly annotated birdsong audio dataset, that is comprised of record- ings containing bird vocalisations along with their active species tags plus the temporal annotations acquired for them. Statistical information about the recordings, their species spe- cific tags and their temporal annotations are presented along with example uses. NIPS4Bplus could be used in various ecoacoustic tasks, such as training models for bird popula- tion monitoring, species classification, birdsong vocalisation detection and classification.

}, keywords = {audio dataset, bird vocalisa- tions, ecoacoustics, ecosystems, rich annotations}, author = {Morfi, Veronica and Bas, Yves and Pamu{\l}a, Hanna and Glotin, Herv{\'e} and Stowell, Dan} }