Changes in 63Hz third-octave band sound levels over 42months recorded at four deep-ocean observatories

Schaar, Mike Connor Roger Malcolm Van der; Ainslie, Michael A.; Robinson, Stephen; Prior, Mark K.; André, M.

doi:10.1016/j.jmarsys.2013.07.008

Cited by 21 publications

(7 citation statements)

References 41 publications

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“…As concern about underwater noise increased, numerous studies have been presented analyzing ocean sound [5], [6], [7], [8]. Commercial off-the-shelf hydrophones used in ocean sound monitoring usually provide raw acoustic data, whether as acoustic recordings or in streaming mode.…”

Section: A Ocean Sound Measurement Systemsmentioning

confidence: 99%

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Metadata-Driven Universal Real-Time Ocean Sound Measurement Architecture

et al. 2021

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Underwater sound in the oceans has been significantly rising in the past decades due to an increase in human activities, adversely affecting the marine environment. In order to assess and limit the impact of underwater noise, the European Commission's Marine Strategy Framework Directive (MSFD) included the long-term monitoring of low-frequency underwater sound as a relevant indicator to achieve a good environmental status. There is a wide range of commercial hydrophones and observing platforms able to perform such measurements. However, heterogeneity and lack of standardization in both hydrophones and observing platforms makes the integration and data management tasks time-consuming and error-prone. Moreover, their power and communications constraints need to be addressed to make them suitable for long-term ocean sound monitoring. Measured underwater sound levels are challenging to compare because different measurement methodologies are used, leading to a risk of misunderstandings and data misinterpretation. Furthermore, the exact methodology applied is not always public or accessible, significantly reducing ocean sound data re-usability. Within this work, a universal architecture for ocean sound measurement is presented, addressing hydrophone integration, real-time in situ processing and data management challenges. Emphasis is placed on generic and re-usable components, so it can be seamlessly replicated and deployed in new scenarios regardless of the underlying hardware and software constraints (hydrophone model, observing platform, operating system, etc.). Within the proposed architecture, a generic implementation of an underwater sound algorithm based on underwater noise measurement best practices is provided. Standardized and coherent metadata with emphasis on strong semantics is discussed, providing the building blocks for FAIR (Findable, Accessible, Interoperable, Reusable) ocean sound data management.

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Section: A Ocean Sound Measurement Systemsmentioning

confidence: 99%

“…In order to correct this loss of amplitude the result of the DFT has to be scaled with the coherent gain (CG). This factor can be calculated as the sum of the window components (W ) normalized by the number of samples (N ), as showed in (7) and (8).…”

Section: B 1/3 Octave Band Frequenciesmentioning

confidence: 99%

Metadata-Driven Universal Real-Time Ocean Sound Measurement Architecture

et al. 2021

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“…Initially, they help to establish the baseline levels of sound. Both deep-ocean observatories 12,13 and autonomous recording systems 14–16 have been used for monitoring. Ideally the monitoring of underwater sound can be imagined to be a network of cabled monitoring stations that sufficiently cover a given marine area.…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Variability of Ambient Underwater Sound in the Baltic Sea

Mustonen

Klauson

Andersson

et al. 2019

Sci Rep

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During last decades, anthropogenic underwater sound and its chronic impact on marine species have been recognised as an environmental protection challenge. At the same time, studies on the spatial and temporal variability of ambient sound, and how it is affected by biotic, abiotic and anthropogenic factors are lacking. This paper presents analysis of a large-scale and long-term underwater sound monitoring in the Baltic Sea. Throughout the year 2014, sound was monitored in 36 Baltic Sea locations. Selected locations covered different natural conditions and ship traffic intensities. The 63 Hz, 125 Hz and 2 kHz one-third octave band sound pressure levels were calculated and analysed. The levels varied significantly from one monitoring location to another. The annual median sound pressure level of the quietest and the loudest location differed almost 50 dB in the 63 Hz one-third octave band. Largest difference in the monthly medians was 15 dB in 63 Hz one-third octave band. The same monitoring locations annual estimated probability density functions for two yearly periods show strong similarity. The data variability grows as the averaging time period is reduced. Maritime traffic elevates the ambient sound levels in many areas of the Baltic Sea during extensive time periods.

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“…An increase has also been observed in low frequency levels (2 dB to 3 dB in the range 5 Hz to 115 Hz) in the Indian Ocean over the last decade [ 9 , 10 ]. More recently, observations in the Northeast Pacific Ocean show a constant level or slightly decreasing trend in low frequency noise [ 11 ], and observations from deep-water stations installed in the Atlantic, Pacific and Indian oceans could not clearly identify common trends between the stations [ 12 , 13 ], making it difficult to make generalised statements about global changes in ocean noise. In general, deep water observations of low frequency trends are influenced by noise from ice breaking, whales and noise from geophysical surveys, but noise from ship traffic is also an influence [ 9 , 12 ] and the growth in ship traffic is widely considered as a potentially significant influence factor in global ocean noise levels [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Anthropogenic Ocean Sound from Shipping Using an Acoustic Sensor Network and a Compressive Sensing Approach

Harris

Philip

Robinson

et al. 2016

Sensors

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Monitoring ocean acoustic noise has been the subject of considerable recent study, motivated by the desire to assess the impact of anthropogenic noise on marine life. A combination of measuring ocean sound using an acoustic sensor network and modelling sources of sound and sound propagation has been proposed as an approach to estimating the acoustic noise map within a region of interest. However, strategies for developing a monitoring network are not well established. In this paper, considerations for designing a network are investigated using a simulated scenario based on the measurement of sound from ships in a shipping lane. Using models for the sources of the sound and for sound propagation, a noise map is calculated and measurements of the noise map by a sensor network within the region of interest are simulated. A compressive sensing algorithm, which exploits the sparsity of the representation of the noise map in terms of the sources, is used to estimate the locations and levels of the sources and thence the entire noise map within the region of interest. It is shown that although the spatial resolution to which the sound sources can be identified is generally limited, estimates of aggregated measures of the noise map can be obtained that are more reliable compared with those provided by other approaches.

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Changes in 63Hz third-octave band sound levels over 42months recorded at four deep-ocean observatories

Cited by 21 publications

References 41 publications

Metadata-Driven Universal Real-Time Ocean Sound Measurement Architecture

Metadata-Driven Universal Real-Time Ocean Sound Measurement Architecture

Spatial and Temporal Variability of Ambient Underwater Sound in the Baltic Sea

Monitoring Anthropogenic Ocean Sound from Shipping Using an Acoustic Sensor Network and a Compressive Sensing Approach

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