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Marine traffic is an important noise contributor to the underwater soundscape. In addition to the growing marine traffic worldwide, the increase in both ship size and ship power tends to intensify the radiated noise, and thus the low-frequency noise level. Studies have shown that marine species can be impacted by the rapid growth of ambient noise levels. In particular, masking of communication or behavior changes in relation to noise exposure have been documented.Shipping noise and its consequences on marine fauna are now monitored, and their evolution is of major concern for marine policies. Shipping noise modeling, based on Automatic Identification System (AIS) data is the most common approach to providing underwater noise levels at basin scales. Indeed, most ships are equipped with AIS transmitters, sending information concerning both the ship (ID, activity, length, etc.) and its navigation (position, speed, heading, etc.). This information is used to compute traffic density maps, and to model the ship’s radiated noise. The ambient noise is finally inferred by propagating noise sources in the environment.In France, a first Covid-19 lockdown occurred from the 17th of March to the 11th of May 2020, reducing significantly the marine traffic. In particular, cruise ships, passenger vessels, vehicle carriers and containerships harbor callings have decreased by 27%, 13%, 7% and 2 % respectively in member states harbors from 2019 to 2020, according to the European Maritime Safety Agency (EMSA). This traffic decrease affected only specific categories of ships, and contributed to some extent to a decrease of the anthropogenic underwater noise.This study aims to analyze the impact of the traffic density reduction due to the lockdown on the shipping noise in the occidental part of the Mediterranean Sea by use of AIS datasets from 2019 and 2020, and eventually to discuss the potential benefit of traffic density reduction as a mitigation measure.

The description of underwater soundscape is central to the understanding of the marine environment, both from the standpoint of the fauna and anthropic activities and its interactions with the atmosphere. Some of these sources produce signals whose patterns are periodically repeated over time (i.e., ship propellers in motion, odontocetes clicks, snapping shrimp, noise emanating from surface waves, etc.). As ocean noise is a combination of various sources sometimes sharing the same frequency band, it is necessary to develop efficient algorithms to process the increasingly voluminous data acquired. To this end, the theory of cyclostationarity is adopted as an effective tool for exposing hidden periodicities in low signal to noise ratio. This theory, widely used to analyze mechanical systems or communications, is extended and applied on underwater soundscapes. The method is demonstrated using data recorded in the Celtic Sea at the French coast of Brittany with practical experiments using field measurements obtained from recording stations.

Underwater noise pollution from impulsive sources (e.g. explosions, seismic airguns, percussive pile driving) can affect marine fauna through mortality, physical injury, auditory damage, physiological stress, acoustic masking, and behavioural responses. Given the potential for large-scale impact on marine ecosystems, some countries are now monitoring impulsive noise activity, coordinated internationally through Regional Seas Conventions. Here, we assess impulsive noise activity in the Northeast Atlantic reported during 2015–2017 to the first international impulsive noise register (INR), established in 2016 under the OSPAR Convention. Seismic airgun surveys were the dominant noise source (38%–56% of annual activity) and declined by 23% during 2015–2017. Reported pile driving activity increased 72%. Explosions and sonar/acoustic deterrent devices both had year-on-year increases in reported activity. Some increases were attributable to more comprehensive reporting in later years. We discuss utilising the INR for risk assessment, target setting, and forward planning, and the implementation of similar systems in other regions.

With the aim of model and predict the underwater noise generated by the shipping traffic, the assessment of the traffic density is adopted in this study. This assessment is realised by using AIS data sets, where each AIS includes vessel name, details, location, speed, among other information. In order to analyse the influence of the comprehensiveness of AIS data sets on the modeled underwater noise, different AIS data sets are considered in this study. The results reveal that traffic noise modelling is strongly degraded by a lack of AIS positions, and data fusion as well as vessel pattern knowledge are greatly suggested to fill these lacks.

International audience; In order to detect and locate a receiver relative to a grid of transmitters in an underwater environment, it is necessary to detect each signal emitted by each transmitter and estimate its Time Of Arrival (TOA) and estimate the position through triangulation. The choice of the signals and their modulation is then crutial to have a good estimation of the TOA and reduce false alarms. This paper focuses on FHSS (Frequency Hopped Spread Spectrum) modulations and compares the performances of several codes, by using a criterion proposed in this paper. It proposes finally to adapt the best FHSS signal in a multi-users context.

International audience; In this paper, we propose a test of hypothesis improvement, by phase study of the analytical cross-correlation function in acoustical detection application. Robustness of false alarms probability for the Time Of Arrival (TOA) estimation represents the goal of the proposed method. After signal detection, TOA will be used to localize one receiver, thanks to a grid of transmitters (more than 3), thanks to the knowledge of positions and transmissions times. The presented method is based on a priori information of the researched signal, forming the correlation signal shape (duration and bandwidth). Knowing the auto-correlation peak shape, we will estimate a range with the cross-correlation peak, and deduce if the detected peak corresponds to the researched signal or to a strong noise.

International audience; In an underwater acoustical context, our application concerns a system immersed, with Nt transmitters and Nr slowly moving receivers. The objective is that all receivers detect the transmitted signals, to estimate the time of arrival (TOA) and then to make possible the location with several TOA (more than 3). To use the CDMA (Code Division Multiple Access) modulation, specially adapted to our problem, we have to choose a method to generate a number Ns of broad-band signals. This study is devoted to the selection of Nt signals among the Ns. The aim is to choose the most distinctly detectable ones. Firstly, in a no Doppler context, the criterion of signals selection is based on a ratio between maximum of auto-correlation and cross-correlation. Secondly, in presence of Doppler, we rely on Ambiguity Function which allows representing the correlation function to several frequency Doppler shifts. The choice of Nt signals is then based on ratio between maximum of auto-ambiguity and cross-ambiguity. This paper will then highlight the interest of the criteria (correlation, ambiguity function) in the choice of the most appropriate signals in function of the multi-users context.