@article {58191, title = {Comparison of methods for rhythm analysis of complex animals{\textquoteright} acoustic signals}, year = {2020}, abstract = {

Analyzing the rhythm of animals\’ acoustic signals is of interest to a growing number of researchers: evolutionary biologists want to disentangle how these structures evolved and what patterns can be found, and ecologists and conservation biologists aim to discriminate cryptic species on the basis of parameters of acoustic signals such as temporal structures. Temporal structures are also relevant for research on vocal production learning, a part of which is for the animal to learn a temporal structure. These structures, in other words, these rhythms, are the topic of this paper. How can they be investigated in a meaningful, comparable and universal way? Several approaches exist. Here we used five methods to compare their suitability and interpretability for different questions and datasets and test how they support the reproducibility of results and bypass biases. Three very different datasets with regards to recording situation, length and context were analyzed: two social vocalizations of Neotropical bats (multisyllabic, medium long isolation calls of Saccopteryx bilineata, and monosyllabic, very short isolation calls of Carollia perspicillata) and click trains of sperm whales, Physeter macrocephalus. Techniques to be compared included Fourier analysis with a newly developed goodness-of-fit value, a generate-and-test approach where data was overlaid with varying artificial beats, and the analysis of inter-onset-intervals and calculations of a normalized Pairwise Variability Index (nPVI). We discuss the advantages and disadvantages of the methods and we also show suggestions on how to best visualize rhythm analysis results. Furthermore, we developed a decision tree that will enable researchers to select a suitable and comparable method on the basis of their data.

}, doi = {10.1371/journal.pcbi.1007755}, url = {https://dx.plos.org/10.1371/journal.pcbi.1007755}, author = {Burchardt, Lara S. and Kn{\"o}rnschild, Mirjam}, editor = {Gershman, Samuel J.} } @article {57952, title = {Acoustically estimated size distribution of sperm whales ( Physeter macrocephalus) off the east coast of New Zealand}, year = {2019}, abstract = {

The length-frequency distribution of sperm whales (Physeter macrocephalus) was studied on the east coast of NZ using passive acoustic recorders moored offshore of Kaikoura, Cape Palliser and Castlepoint. Sperm whale\’s echolocation signals are unique among odontocete species. Their clicks are composed by multiple pulses resulting from the sound transmission within the whale head. The total length of the whales can be estimated by measuring the time delay between these pulses. A total of 997 length measurements were obtained from click trains using cepstral analysis (mean\ =\ 14.6 m; min\ =\ 9.6 m; max\ =\ 18.3 m; std\ =\ 1 m). The size-frequency distributions at all three locations were similar, although animals smaller than 12 m were not present offshore of Kaikoura. Animals of various sizes appeared to be present all year round, with no apparent seasonality in the occurrence of any size class.

}, keywords = {cepstral analysis, ceptrum, echolocation, length-frequency distribution, marine mammal, odontocete, PAM, passive acoustics, sperm whale}, doi = {10.1080/00288330.2019.1679843}, url = {https://www.tandfonline.com/doi/full/10.1080/00288330.2019.1679843https://www.tandfonline.com/doi/pdf/10.1080/00288330.2019.1679843}, author = {Giorli, Giacomo and Goetz, Kimberly T.} } @article {57951, title = {Foraging activity of sperm whales (Physeter macrocephalus) off the east coast of New Zealand}, year = {2019}, abstract = {

The occurrence and distribution of sperm whales in New Zealand waters is mainly known from whaling records or opportunistic sightings by the public and a systematic estimation of the abundance and distribution has never been conducted. In this study, we investigated the foraging activity and occurrence of sperm whales off the Eastern coast of New Zealand using passive acoustic monitoring techniques. Three acoustic recorders were moored to the ocean floor at different locations on the east side of the North and South Island to collect passive acoustic data from June 2016 until August 2017. A total of 53,823 echolocation click trains were recorded and analyzed to understand the spatial and temporal variation of sperm whale foraging activity. No difference in the foraging activity was found between night-time and day-time periods at any of the locations. Click train detections increased toward the south, suggesting increased foraging activity near Kaikoura. At each station, sperm whale foraging activity varied by month.

}, doi = {10.1038/s41598-019-48417-5}, url = {http://www.nature.com/articles/s41598-019-48417-5}, author = {Giorli, Giacomo and Goetz, Kimberly T.} } @article {57363, title = {Deep Machine Learning Techniques for the Detection and Classification of Sperm Whale Bioacoustics}, journal = {Scientific Reports}, volume = {9}, year = {2019}, month = {Jan-12-2019}, abstract = {

We implemented Machine Learning (ML) techniques to advance the study of sperm whale (Physeter macrocephalus) bioacoustics. This entailed employing Convolutional Neural Networks (CNNs) to construct an echolocation click detector designed to classify spectrograms generated from sperm whale acoustic data according to the presence or absence of a click. The click detector achieved 99.5\% accuracy in classifying 650 spectrograms. The successful application of CNNs to clicks reveals the potential of future studies to train CNN-based architectures to extract finer-scale details from cetacean spectrograms. Long short-term memory and gated recurrent unit recurrent neural networks were trained to perform classification tasks, including (1) \“coda type classification\” where we obtained 97.5\% accuracy in categorizing 23 coda types from a Dominica dataset containing 8,719 codas and 93.6\% accuracy in categorizing 43 coda types from an Eastern Tropical Pacific (ETP) dataset with 16,995 codas; (2) \“vocal clan classification\” where we obtained 95.3\% accuracy for two clan classes from Dominica and 93.1\% for four ETP clan types; and (3) \“individual whale identification\” where we obtained 99.4\% accuracy using two Dominica sperm whales. These results demonstrate the feasibility of applying ML to sperm whale bioacoustics and establish the validity of constructing neural networks to learn meaningful representations of whale vocalizations.

}, doi = {10.1038/s41598-019-48909-4}, url = {http://www.nature.com/articles/s41598-019-48909-4}, author = {Bermant, Peter C. and Bronstein, Michael M. and Wood, Robert J. and Gero, Shane and Gruber, David F.} } @article {53493, title = {Automatic acoustic estimation of sperm whale size distributions achieved through machine recognition of on-axis clicks}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-12-2018}, pages = {3485 - 3495}, abstract = {

The waveforms of individual sperm whale clicks often appear as multiple pulses, which are the product of a single pulse reverberating throughout the spermaceti organ. Since there is a relationship between spermaceti organ size and total body size, it is possible to estimate a whale\&$\#$39;s length by measuring the inter-pulse intervals (IPIs) within its clicks. However, if a click is recorded off-axis, the IPI corresponding to spermaceti organ length is usually obscured. This paper presents an algorithm for automatically estimating the \“true\” IPIs of sperm whales in a recording by measuring them from on-axis clicks only. The routine works by classifying detected clicks with a support vector machine, assessing the stability of their IPIs, and then clustering the stable IPIs using Gaussian mixture models. Results show that the routine is very accurate in obtaining reliable IPIs, but has a high false negative rate. Nonetheless, since sperm whales click very frequently, it is possible to obtain useful IPI distributions with only a few minutes of recording. This algorithm makes it possible to estimate the body lengths of multiple sperm whales automatically with only one hydrophone. An implementation is available for download at http://whitelab.biology.dal.ca/CABLE/cable.htm.

}, issn = {0001-4966}, doi = {10.1121/1.5082291}, url = {http://asa.scitation.org/doi/10.1121/1.5082291}, author = {Beslin, Wilfried A. M. and Whitehead, Hal and Gero, Shane} } @article {53371, title = {Kinship and association do not explain vocal repertoire variation among individual sperm whales or social units}, journal = {Animal Behaviour}, volume = {145}, year = {2018}, month = {Jan-11-2018}, pages = {131 - 140}, abstract = {

Vocal learning often results in distinct dialects among individuals or groups, but the forces selecting for these phenomena remain unclear. Female sperm whales, Physeter macrocephalus, and their dependent offspring live in matrilineally based social units, and the units associate within sympatric clans. The clans have distinctive dialects of codas (patterns of clicks), as do, to a lesser extent, the units within clans. We examined the similarity of coda repertoires of individuals and units from the eastern Caribbean and related these to patterns of kinship and social association. Similarity in coda repertoires was not discernibly correlated with close kinship or association rates for either individuals or units (matrix correlation coefficients \<0.12 for all tests using whole repertoires and data from all units). This supports the prevailing hypothesis that these vocalizations are culturally transmitted. The lack of correlation also indicates that vocal learning may occur broadly within clans, rather than preferentially from close kin or close social associates within social units, or that biases in vocal learning at lower levels of social structure are diffused by clan-level processes, such as conformity. Finally, an absence of signals of kinship in vocalization patterns suggests that a different mechanism, perhaps familiarity through repeated association, mediates kin selection among sperm whales.

}, keywords = {dialect, kinship, matrilineal, social unit, sperm whale, vocal learning, vocalization}, issn = {00033472}, doi = {10.1016/j.anbehav.2018.09.011}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0003347218302811https://api.elsevier.com/content/article/PII:S0003347218302811?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0003347218302811?httpAccept=text/plain}, author = {Konrad, Christine M. and Frasier, Timothy R. and Rendell, Luke and Whitehead, Hal and Gero, Shane} } @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 {53267, title = {Sperm whale inter-pulse intervals and size: The anomaly called Yukusam}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-09-2018}, pages = {1979 - 1979}, abstract = {

Yukusam is the name given to a male sperm whale who was first documented off northeastern Vancouver Island in February 2018. He spent several weeks in this area before traveling south to the inland waters of the Salish Sea in late March 2018. Sperm whale clicks have been used as a proxy to determine overall size by using the time difference of arrival between the initial noise pulse and its reflections within the spermaceti organ. Different equations have been derived in order to use this Inter-Pulse Interval (IPI) to estimate overall length. Here we use Yukusam\’s usual clicks recorded from the Lime Kiln hydrophone on the west side of San Juan Island (WA State, USA) to compare IPI-based length estimates with visual observations. Photo and video documentation indicate that this whale is ~15 m in length. Our initial acoustic results indicate that equations in the literature underestimate his length by at least a few meters. The potential reasons behind these anomalies are explored in this presentation.

}, issn = {0001-4966}, doi = {10.1121/1.5068640}, url = {http://asa.scitation.org/doi/10.1121/1.5068640}, author = {Turner, Jesse C. and Wood, Jason and Towers, Jared} } @article {53024, title = {Variability of the inter-pulse interval in sperm whale clicks with implications for size estimation and individual identification}, journal = {The Journal of the Acoustical Society of America}, volume = {144}, year = {2018}, month = {Jan-07-2018}, pages = {365 - 374}, abstract = {

Sperm whales generate multi-pulsed clicks for echolocation and communication with an inter-pulse interval (IPI) determined by the size of their hypertrophied sound producing nose. The IPI has therefore been used to estimate body size and distinguish between individuals, and it has been hypothesized that conspecifics may use IPIs to recognize each other. However, the degree to which IPIs vary within individuals has not explicitly been tested, and therefore the inherent precision of this measure and its applicability for size estimation for researchers and sperm whales alike remain unknown. Here, the variability in IPI from both animal-borne Dtags and far-field recordings from echolocating and communicating sperm whales is quantified. Three different automatic methods (envelope, cepstrum, and cross-correlation) are tested and it is found that the envelope approach results in the least dispersion. Furthermore, it is shown that neither growth, depth, nor recording aspect fully explains the observed variability among clicks recorded from the same individual. It is proposed that dynamics in the soft structures of the nose are affecting IPIs, resulting in a variation of approximately 0.2\ ms. Therefore, it is recommended that this variation be considered in IPI studies and that IPIs may have limited functionality as an identity cue among large groups of conspecifics.

}, issn = {0001-4966}, doi = {10.1121/1.5047657}, url = {http://asa.scitation.org/doi/10.1121/1.5047657}, author = {B{\o}ttcher, Anne and Gero, Shane and Beedholm, Kristian and Whitehead, Hal} }