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Olivier Adam

Bio: Olivier Adam is an academic researcher from University of Paris. The author has contributed to research in topics: Whale & Humpback whale. The author has an hindex of 16, co-authored 61 publications receiving 682 citations. Previous affiliations of Olivier Adam include University of Paris-Sud & Université Paris-Saclay.


Papers
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Journal ArticleDOI
TL;DR: A previously used pattern recognition technique relying on cross-correlation with a template was modified in order to include a priori information allowing detection accuracy, and BAEPs detection was enhanced from 76 to 90%.

61 citations

Journal ArticleDOI
TL;DR: Using acoustic data from an International Monitoring System hydroa- coustic station, it is found that Antarctic and pygmy blue whales occur sympatrically in this area based on detection of their stereotyped calls, which contradicts the migration paradigm attributed to this subspecies.
Abstract: In the Southern Indian Ocean, 2 subspecies of blue whales Balaenoptera musculus spp. occur, the Antarctic B. m. intermedia and the pygmy blue whale B. m. brevicauda. Until the present study was conducted it was assumed that the distribution of these 2 subspecies was delimited by the Antarctic Convergence (52° to 56° S) during the austral summer. Here, we report results from the first year-long, continuous acoustic monitoring of blue whales in mid-latitude waters off the Crozet Islands (46° 25' S, 51° 40' E). Using acoustic data from an International Monitoring System hydroa- coustic station, we found that Antarctic and pygmy blue whales occur sympatrically in this area based on detection of their stereotyped calls. Antarctic blue whale calls were recorded year-round, indicat- ing continuous presence in the region, which contradicts the migration paradigm attributed to this subspecies. Three geographically distinct types of pygmy blue whale calls were recorded during the summer and autumn only. The Madagascan call type was the most frequently recorded, while Sri Lankan and Australian call types recorded in this area suggest basin-scale longitudinal and latitudi- nal movements. During spring and summer, blue whale calls were often associated with higher fre- quency sounds (D calls), which have been attributed to feeding activity. The summer co-occurrence of blue whale subspecies highlights the importance of this productive sub-Antarctic area as a blue whale hotspot and provides new insights into blue whale seasonal distribution and segregation.

59 citations

Journal ArticleDOI
TL;DR: In the Southwestern Indian Ocean, one year of continuous acoustic data from calibrated hydrophones maintained by the International Monitoring System provided data on blue whale calls from two subspecies Antarctic and pygmy blue whales.

51 citations

Journal ArticleDOI
TL;DR: These are the first reported source level estimations for blue whales in the Indian Ocean, and slight variations in the source level could be due to inter-individual differences, inter-subspecies variations and the calculation method.
Abstract: Blue whales produce intense, stereotypic low frequency calls that are particularly well suited for transmission over long distances. Because these calls vary geographically, they can be used to gain insight into subspecies distribution. In the Southwestern Indian Ocean, acoustic data from a triad of calibrated hydrophones maintained by the International Monitoring System provided data on blue whale calls from two subspecies: Antarctic and pygmy blue whales. Using time difference of arrival and least-squares hyperbolic methods, the range and location of calling whales were determined. By using received level of calls and propagation modeling, call source levels of both subspecies were estimated. The average call source level was estimated to 179±5 dB re 1 μParms at 1 m over the 17–30 Hz band for Antarctic blue whale and 174±1 dB re 1 μParms at 1 m over the 17–50 Hz band for pygmy blue whale. According to previous estimates, slight variations in the source level could be due to inter-individual differences,...

43 citations

Journal ArticleDOI
TL;DR: In this article, the authors proposed a model to predict the sound unit durations and frequency formants of humpback whales using measurements of the trachea, laryngeal sac, and nasal cavities.

42 citations


Cited by
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01 Mar 1995
TL;DR: This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series and results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages.
Abstract: : This thesis applies neural network feature selection techniques to multivariate time series data to improve prediction of a target time series. Two approaches to feature selection are used. First, a subset enumeration method is used to determine which financial indicators are most useful for aiding in prediction of the S&P 500 futures daily price. The candidate indicators evaluated include RSI, Stochastics and several moving averages. Results indicate that the Stochastics and RSI indicators result in better prediction results than the moving averages. The second approach to feature selection is calculation of individual saliency metrics. A new decision boundary-based individual saliency metric, and a classifier independent saliency metric are developed and tested. Ruck's saliency metric, the decision boundary based saliency metric, and the classifier independent saliency metric are compared for a data set consisting of the RSI and Stochastics indicators as well as delayed closing price values. The decision based metric and the Ruck metric results are similar, but the classifier independent metric agrees with neither of the other metrics. The nine most salient features, determined by the decision boundary based metric, are used to train a neural network and the results are presented and compared to other published results. (AN)

1,545 citations

25 Apr 2017
TL;DR: This presentation is a case study taken from the travel and holiday industry and describes the effectiveness of various techniques as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib).
Abstract: This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

1,338 citations

Journal ArticleDOI
TL;DR: In this paper, the authors present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field, and provide a framework for acoustics-based density estimation, illustrated with real-world case studies.
Abstract: Reliable estimation of the size or density of wild animal populations is very important for effective wildlife management, conservation and ecology. Currently, the most widely used methods for obtaining such estimates involve either sighting animals from transect lines or some form of capture-recapture on marked or uniquely identifiable individuals. However, many species are difficult to sight, and cannot be easily marked or recaptured. Some of these species produce readily identifiable sounds, providing an opportunity to use passive acoustic data to estimate animal density. In addition, even for species for which other visually based methods are feasible, passive acoustic methods offer the potential for greater detection ranges in some environments (e.g. underwater or in dense forest), and hence potentially better precision. Automated data collection means that surveys can take place at times and in places where it would be too expensive or dangerous to send human observers. Here, we present an overview of animal density estimation using passive acoustic data, a relatively new and fast-developing field. We review the types of data and methodological approaches currently available to researchers and we provide a framework for acoustics-based density estimation, illustrated with examples from real-world case studies. We mention moving sensor platforms (e.g. towed acoustics), but then focus on methods involving sensors at fixed locations, particularly hydrophones to survey marine mammals, as acoustic-based density estimation research to date has been concentrated in this area. Primary among these are methods based on distance sampling and spatially explicit capture-recapture. The methods are also applicable to other aquatic and terrestrial sound-producing taxa. We conclude that, despite being in its infancy, density estimation based on passive acoustic data likely will become an important method for surveying a number of diverse taxa, such as sea mammals, fish, birds, amphibians, and insects, especially in situations where inferences are required over long periods of time. There is considerable work ahead, with several potentially fruitful research areas, including the development of (i) hardware and software for data acquisition, (ii) efficient, calibrated, automated detection and classification systems, and (iii) statistical approaches optimized for this application. Further, survey design will need to be developed, and research is needed on the acoustic behaviour of target species. Fundamental research on vocalization rates and group sizes, and the relation between these and other factors such as season or behaviour state, is critical. Evaluation of the methods under known density scenarios will be important for empirically validating the approaches presented here.

483 citations