02413nas a2200253 4500008004100000245012800041210006900169520160500238653001701843653001001860653000901870653001101879653001501890100002101905700002501926700001801951700002001969700002201989700002402011700002002035700002202055700002102077856006102098 2019 eng d00aPassive monitoring of phenological acoustic patterns reveals the sound of the camouflage grouper, Epinephelus polyphekadion0 aPassive monitoring of phenological acoustic patterns reveals the3 a

1. Passive acoustic monitoring (PAM) is a non‐invasive technique that uses hydro- phones to monitor populations and ecosystem dynamics. Although many applica- tions of PAM have been developed in recent years, it has never been used to identify a calling marine species.
2. ThesouthpassofFakaravaAtoll,FrenchPolynesia,hostsspawningeventsofmany reef fish species, including the camouflage grouper Epinephelus polyphekadion, with a spawning aggregation abundance exceeding 17 000 individuals during the full moons of June and July.
3. The current study aimed to use PAM to distinguish camouflage grouper sounds among the vocal activities of all fish recorded during the aggregation periods. Audio recordings analysis resulted in the identification of 29 sound types, some of which showed diel and lunar patterns.
4. Temporal analysis of these sounds in relation to spawning activities allowed the identification of camouflage grouper calls. These calls can be described as a single pulse or a series of ‘boom(s)’ with a pulse duration of ~44 ms and a low dominant frequency of 103 ± 31 Hz. Video recordings show that the camouflage grouper pro- duces the ‘booms’ to initialize spawner ascent and to promote synchronous gamete release into the water column.
5. The study highlights for the first time that PAM can be used to identify the previ- ously unknown sound of a fish species. Moreover, we can use it to understand the phenology of some biological activities for improving the resolution of fish bio- diversity assessments.

10abiodiversity10acoral10afish10alagoon10amonitoring1 aJublier, Noémie1 aBertucci, Frédéric1 aKéver, Loïc1 aColleye, Orphal1 aBallesta, Laurent1 aNemeth, Richard, S.1 aLecchini, David1 aRhodes, Kevin, L.1 aParmentier, Eric uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/aqc.324202024nas a2200181 4500008004100000022001400041245013600055210006900191260001600260300001600276490000800292520140700300100002201707700002001729700001901749700002301768856005101791 2018 eng d a0001-496600aAutomatic detection of fish sounds based on multi-stage classification including logistic regression via adaptive feature weighting0 aAutomatic detection of fish sounds based on multistage classific cJan-11-2018 a2709 - 27180 v1443 a

This paper presents a method for automatic detection of fish sounds in an underwater environment. There exist two difficulties: (i) features and classifiers that provide good detection results differ depending on the underwater environment and (ii) there are cases where a large amount of training data that is necessary for supervised machine learning cannot be prepared. A method presented in this paper (the proposed hybrid method) overcomes these difficulties as follows. First, novel logistic regression (NLR) is derived via adaptive feature weighting by focusing on the accuracy of classification results by multiple classifiers, support vector machine (SVM), and k-nearest neighbors (k-NN). Although there are cases where SVM or k-NN cannot work well due to divergence of useful features, NLR can produce complementary results. Second, the proposed hybrid method performs multi-stage classification with consideration of the accuracy of SVM, k-NN, and NLR. The multi-stage acquisition of reliable results works adaptively according to the underwater environment to reduce performance degradation due to diversity of useful classifiers even if abundant training data cannot be prepared. Experiments on underwater recordings including sounds of Sciaenidae such as silver croakers (Pennahia argentata) and blue drums (Nibea mitsukurii) show the effectiveness of the proposed hybrid method.

1 aHarakawa, Ryosuke1 aOgawa, Takahiro1 aHaseyama, Miki1 aAkamatsu, Tomonari uhttp://asa.scitation.org/doi/10.1121/1.506737301962nas a2200229 4500008004100000022001400041245010300055210006900158260001600227300001400243490000800257520124200265100002101507700002701528700001801555700003501573700002201608700001801630700001501648700001801663856005101681 2018 eng d a0001-496600aAn approach for automatic classification of grouper vocalizations with passive acoustic monitoring0 aapproach for automatic classification of grouper vocalizations w cJan-02-2018 a666 - 6760 v1433 a

Grouper, a family of marine fishes, produce distinct vocalizations associated with their reproductive behavior during spawning aggregation. These low frequencies sounds (50–350 Hz) consist of a series of pulses repeated at a variable rate. In this paper, an approach is presented for automatic classifica- tion of grouper vocalizations from ambient sounds recorded in situ with fixed hydrophones based on weighted features and sparse classifier. Group sounds were labeled initially by humans for training and testing various feature extraction and classification methods. In the feature extraction phase, four types of features were used to extract features of sounds produced by groupers. Once the sound features were extracted, three types of representative classifiers were applied to categorize the spe- cies that produced these sounds. Experimental results showed that the overall percentage of identifi- cation using the best combination of the selected feature extractor weighted mel frequency cepstral coefficients and sparse classifier achieved 82.7% accuracy. The proposed algorithm has been imple- mented in an autonomous platform (wave glider) for real-time detection and classification of group vocalizations.

1 aIbrahim, Ali, K.1 aChérubin, Laurent, M.1 aZhuang, Hanqi1 aUmpierre, Michelle, T. Schäre1 aDalgleish, Fraser1 aErdol, Nurgun1 aOuyang, B.1 aDalgleish, A. uhttp://asa.scitation.org/doi/10.1121/1.5022281