02781nas a2200181 4500008004100000245010000041210006900141520217000210653002102380653001702401653002302418653002102441653002202462653002202484100001902506700001302525856006102538 2019 eng d00aSource separation in ecoacoustics: A roadmap towards versatile soundscape information retrieval0 aSource separation in ecoacoustics A roadmap towards versatile so3 a

A comprehensive assessment of ecosystem dynamics requires the monitoring of biological, physical and social changes. Changes that cannot be observed visually may be trackable acoustically through soundscape analysis. Soundscapes vary greatly depending on geophysical events, biodiversity and human activities. However, retrieving source‐specific information from geophony, biophony and anthropophony remains a challenging task, due to interference by simultaneous sound sources. Audio source separation is a technique that aims to recover individual sound sources when only mixtures are accessible. Here, we review techniques of monoaural audio source separation with the fundamental theories and assumptions behind them. Depending on the availability of prior information about the source signals, the task can be approached as a blind source separation or a model‐based source separation. Most blind source separation techniques depend on assumptions about the behaviour of the source signals, and their performance may deteriorate when the assumptions fail. Model‐based techniques generally do not require specific assumptions, and the models are directly learned from labelled data. With the recent advances of deep learning, the model‐based techniques can yield state‐of‐the‐art separation performance, accordingly facilitate content‐based audio information retrieval. Source separation techniques have been adopted in several ecoacoustic applications to evaluate the contributions from biodiversity and anthropogenic disturbance to soundscape dynamics. They can also be employed as nonlinear filters to improve the recognition of bioacoustic signals. To effectively retrieve ecological information from soundscapes, source separation is a crucial tool. We believe that the future integrations of ecological hypotheses and deep learning can realize a high‐performance source separation for ecoacoustics, and accordingly improve soundscape‐based ecosystem monitoring. Therefore, we outline a roadmap for applying source separation to assist in soundscape information retrieval and hope to promote cross‐disciplinary collaboration.

10aacoustic habitat10abiodiversity10aecosystem dynamics10amachine learning10apassive acoustics10aSignal processing1 aLin, Tzu‐Hao1 aTsao, Yu uhttps://onlinelibrary.wiley.com/doi/abs/10.1002/rse2.14101785nas a2200217 4500008004100000245012800041210006900169520098500238653002201223653001201245653001701257653003401274653001801308653001501326653000801341653002201349653001601371100002001387700002401407856013601431 2019 eng d00aAcoustically estimated size distribution of sperm whales ( Physeter macrocephalus) off the east coast of New Zealand0 aAcoustically estimated size distribution of sperm whales iPhyset3 a

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.

10acepstral analysis10aceptrum10aecholocation10alength-frequency distribution10amarine mammal10aodontocete10aPAM10apassive acoustics10asperm whale1 aGiorli, Giacomo1 aGoetz, Kimberly, T. uhttps://www.tandfonline.com/doi/full/10.1080/00288330.2019.1679843https://www.tandfonline.com/doi/pdf/10.1080/00288330.2019.167984302118nas a2200289 4500008004100000022001300041245018500054210006900239260001600308300001200324490000800336520098500344653001701329653001201346653001201358653002001370653001201390653002001402653002201422100003001444700003201474700002001506700002501526700002701551700001701578856023301595 2019 eng d a0964569100aRecreational boat traffic effects on fish assemblages: First evidence of detrimental consequences at regulated mooring zones in sensitive marine areas detected by passive acoustics0 aRecreational boat traffic effects on fish assemblages First evid cJan-02-2019 a22 - 340 v1683 a

We assess the impact of recreational boat traffic on sensitive habitats in the Western Mediterranean using passive acoustics. We compared underwater sounds in three regulated mooring locations vs a pristine location; and temporal differences in the pristine location vs the nearest mooring between high and low touristic seasons. We measured the number of pulses/minute, fish pulse patterns, and percentage of boat noise occurrence and its relative average power level. At the pristine location, the call rates and their complexity were significantly higher and the motorboat noise was significantly lower. The temporal trend of biophonic sounds decreased significantly in the pristine location. In contrast, in the mooring sites, the motorboat noise decreased significantly and the fish calls remained at low levels in both seasons. In conclusion, motorboat noise negatively affects the complexity of the fish assemblages but could be conditioned to their historic uses.

10abioacoustics10aCroaker10aDrummer10aFish assemblage10aMooring10aMotorboat noise10apassive acoustics1 aCorrea, José, Miguel Gon1 aSempere, Just-Tomás, Bayle1 aJuanes, Francis1 aRountree, Rodney, A.1 aRuíz, Juan, Francisco1 aRamis, Jaime uhttps://linkinghub.elsevier.com/retrieve/pii/S0964569118301972https://api.elsevier.com/content/article/PII:S0964569118301972?httpAccept=text/xmlhttps://api.elsevier.com/content/article/PII:S0964569118301972?httpAccept=text/plain