03492nas a2200253   4500008004100000022001300041245013000054210006900184260001600253300001400269490000700283520249700290653001802787653002702805653002502832653001902857653003202876100001802908700002202926700001502948700002202963700002002985856023303005       2019                        eng d  a1470160X00aAn evaluation of the efficiency of passive acoustic monitoring in detecting deer and primates in comparison with camera traps0 aevaluation of the efficiency of passive acoustic monitoring in d  cJan-03-2019  a753 - 7620 v983 a<p>In recent years, camera traps have rapidly become popular for the large-scale monitoring of wildlife distributionand population; however, we should not ignore the uncertainty regarding the reliability of camera-basedmonitoring by inexperienced data gatherers. This study introduces passive acoustic monitoring (PAM) as aneasier technique for monitoring terrestrial mammals that uses the sound cues that they produce. To validate theefficacy of PAM, we quantitatively compared the detection areas and rates between sound cues (from PAM) andvisual cues (from camera traps) of two mammals&mdash;the sika deerCervus nipponand the Japanese macaqueMacacafuscata&mdash;across seven study sites in eastern Japan with different population densities. To collect sound cues, weset up multiple autonomous recording units at the sites and continuously recorded ambient sounds, following apre-determined schedule. The total recording time reached 9081 h for deer and 8235 h for macaques. We thenbuilt sound recognizers to automatically detect eight target call types from the recorded data. To collect visualcues, we also set multiple camera traps at the same sites and for the same observation periods. The keyfindingswere as follows: (1) the fully automated procedures that only used the recognizers to detect sound cues producednumerous false positive detections when the call type possessed vocal plasticity and variations; (2) the semi-automated procedures, which included an additional step to validate the automated detections by manualscreening, exhibited a great improvement in the detectability and recall rates of the half of the target calls,reaching &gt; 0.70; (3) when using the semi-automated procedures, the frequency of deer and macaque detectionsper trap-day derived from the sound cues were in most cases approximately dozens of times and several times,respectively, higher than that derived from the visual cues; (4) the main advantage of PAM may be its superiordetection areas, which were 100&ndash;7000 times wider than those of camera traps; and (5) the current success of therecognition of different call types of each species could broaden the use of PAM, which is not possible for cameratraps. PAM could provide socio-behavioral data (i.e., the frequencies and types of inter-individual vocal com-munications) that could help understand the status of population dynamics and the group compositions, inaddition to information related to the presence or absence of species.</p>
10aCervus nippon10aEcoacoustic monitoring10aLag-phase management10aMacaca fuscata10apassive acoustic monitoring1 aEnari, Hiroto1 aEnari, Haruka, S.1 aOkuda, Kei1 aMaruyama, Tetsuya1 aOkuda, Kana, N.  uhttps://linkinghub.elsevier.com/retrieve/pii/S1470160X18309257https://api.elsevier.com/content/article/PII:S1470160X18309257?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S1470160X18309257?httpAccept=text/plain03179nas a2200253   4500008004100000022001300041245011000054210006900164260001600233300001400249490000700263520236200270653002302632653002702655653002502682653003402707653001302741100002702754700001902781700001802800700002302818700001802841856006602859       2018                        eng d  a1470160X00aEstimating animal acoustic diversity in tropical environments using unsupervised multiresolution analysis0 aEstimating animal acoustic diversity in tropical environments us  cJan-07-2018  a346 - 3550 v903 a<p>Ecoacoustic monitoring has proved to be a viable approach to capture ecological data related to animal communities. While experts can manually annotate audio samples, the analysis of large datasets can be significantly facilitated by automatic pattern recognition methods. Unsupervised learning methods, which do not require labelled data, are particularly well suited to analyse poorly documented habitats, such as tropical environments. Here we propose a new method, named Multiresolution Analysis of Acoustic Diversity (MAAD), to automate the detection of relevant structure in audio data. MAAD was designed to decompose the acoustic community into few elementary components (soundtypes) based on their time&ndash;frequency attributes. First, we used the short-time Fourier transform to detect regions of interest (ROIs) in the time&ndash;frequency domain. Then, we characterised these ROIs by (1) estimating the median frequency and (2) by running a 2D wavelet analysis at multiple scales and angles. Finally, we grouped the ROIs using a model-based subspace clustering technique so that ROIs were automatically annotated and clustered into soundtypes. To test the performance of the automatic method, we applied MAAD to two distinct tropical environments in French Guiana, a lowland high rainforest and a rock savanna, and we compared manual and automatic annotations using the adjusted Rand index. The similarity between the manual and automated partitions was high and consistent, indicating that the clusters found are intelligible and can be used for further analysis. Moreover, the weight of the features estimated by the clustering process revealed important information about the structure of the acoustic communities. In particular, the median frequency had the strongest effect on modelling the clusters and on classification performance, suggesting a role in community organisation. The number of clusters found in MAAD can be regarded as an estimation of the soundtype richness in a given environment. MAAD is a comprehensive and promising method to automatically analyse passive acoustic recordings. Combining MAAD and manual analysis would maximally exploit the strengths of both human reasoning and computer algorithms. Thereby, the composition of the acoustic community could be estimated accurately, quickly and at large scale.</p>
10aAcoustic community10aEcoacoustic monitoring10aNocturnal soundscape10aUnsupervised machine learning10aWavelets1 aUlloa, Juan, Sebastian1 aAubin, Thierry1 aLlusia, Diego1 aBouveyron, Charles1 aSueur, Jerome  uhttp://linkinghub.elsevier.com/retrieve/pii/S1470160X1830181X