Abstract
Making Ambient Noise Trends Accessible (MANTA) software is a tool for the community to enable comparisons between soundscapes and identification of ambient ocean sound trends required by ocean stakeholders. MANTA enhances the value of individual datasets by assisting users in creating thorough calibration metadata and internationally recommended products comparable over time and space to ultimately assess ocean sound at any desired scale up to a global level. The software package combines of two applications: MANTA Metadata App, which allows users to specify information about their recordings, and MANTA Data Mining App, which applies that information to acoustic recordings to produce consistently processed, calibrated time series products of sound pressure levels in hybrid millidecade bands. The main outputs of MANTA are daily.csv and NetCDF files containing 60-s spectral energy calculations in hybrid millidecade bands and daily statistics images. MANTA data product size and formats enable easy and compact transfer and archiving among researchers and programs, allowing data to be further averaged and explored to address user-specified questions.
Highlights
Sound permeates the ocean and travels to the deepest ocean depths relatively uninhibited compared to light
Making Ambient Noise Trends Accessible is provided for download in two forms that currently run on Windows operating systems1: (1) as a bundled set of MATLAB scripts (i.e., m-files) executed under MATLAB, and (2) as a stand-alone, fully compiled executable that does not require the user to obtain a MATLAB license
User feedback has identified isolated coding bugs that have resulted in new versions of the Making Ambient Noise Trends Accessible (MANTA) software downloads that are tracked by date and version number
Summary
Sound permeates the ocean and travels to the deepest ocean depths relatively uninhibited compared to light. The applied uses of information present in passive acoustic recordings of ocean soundscapes continues to grow as (1) the cost and commercial availability of passive acoustic recorders makes this technology widely accessible (Mellinger et al, 2007; Sousa-Lima et al, 2013; Gibb et al, 2019), (2) storage and battery capacity support longer autonomous deployments, (3) advances in signal processing related to machine learning and artificial intelligence make harvesting valuable information from the large volume of soundscape data tractable (Caruso et al, 2020; Shiu et al, 2020), and (4) national/international policy and regulation recognize ocean sound as an ocean parameter to be managed due to the potential negative impacts on the marine environment (Tasker et al, 2010; Duarte et al, 2021). Innovative ocean sound applications associated with policy and economy include soundscapes being used as a functional management tool (Van Parijs et al, 2009) and as an indicator of global economy and trade (Frisk, 2012; Thomson and Barclay, 2020)
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