Use of noise correlation matrices to interpret ocean ambient noise

Abstract

Oceanic ambient noise is a dynamic mixture of biologic, geophysical, and anthropogenic sound sources. A goal of research is to put some order in this cacophony of information, understand the received spectral content and determine the primary contributors to the ambient noise. This paper compares three methods to assist in that process (with emphasis on noise correlation techniques): noise correlation matrices, manual selection of noise spectra, and principal component analysis. Comparison followed a common process: selection of a replica set (best termed a characteristic subset of noise spectra), which are used to recreate the original noise field for comparison and consequent decision as to whether that replica set represented the noise measurements adequately. Conclusions of this study are (1) noise correlation matrices provide the best definition of the spectra that represent a particular source and offer potential in organizing and identifying specific noise source content. (2) Manual sorting of noise spectra, while able to identify specific events easily, is both labor intensive, given the quantity of data available; and suffers from incorrect interpretation of multiple competing sound sources, when present. (3) Principal component analysis provides the best reconstruction of measured noise, but has difficulty linking components to physical source mechanisms.

Analysing Interlinked Frequency Dynamics of the Urban Acoustic Environment

“…This way, simultaneously occupied frequency bins are visualised. As temporal connections between frequency bins can be interpreted as similar sound sources [ 35 ] (s. 4.2), this method allows the characterisation of urban AEs by identifying interrelationships between frequency bins.…”

“…Correlation matrices are a common tool to describe associations between a plethora of variables and build the foundation, e.g., for connecting cortical hubs in the human brain [ 32 ] or climate dynamics [ 33 ]. Unlike more commonly used methods, such as spectrogram cross-correlation [ 24 ], FCMs have been only seldom used in the field of acoustics, e.g., to describe ocean ambient noise [ 34 , 35 ]. In the urban acoustic environment, high correlations between frequency bands characterise the prevalence of particular sound sources and therefore provide valuable information to distinguish between the AEs of different urban settings.…”

The Urban Acoustic Environment as a Complex Network

“…One of the main advantages of CNs is their ability to capture the emergent properties and behaviors of the system as a whole, rather than focusing solely on individual components [6]. Here, we apply CNs to quantify frequency correlation matrices (FCMs), which have shown to be a promising tool to depict environment specific interrelationships between power spectra in high spatial and temporal resolution [7,8]. The aim of this work is to give an intuitive understanding on how FCM based CNs in the urban AE work.…”

North Sea soundscapes from a fish perspective: Directional patterns in particle motion and masking potential from anthropogenic noise

“…Arrays of multiple sound pressure sensing hydrophones have been used to model the directionality (beam forming) based on correlating the sensor position and variation in sound arrival times (e.g., Cox, 1973;Walker and Buckingham, 2012;Fried et al, 2013;Yang et al, 2018). Also, co-modulation at different frequency bands can be used to deduce spatial information through inference of the source directions and distances from the spectral coherence (Nichols and Sayer, 1977;Nichols and Bradley, 2019;Zhou, 2020). Vector sensors (Wilson et al, 1985;D'Spain et al, 2006;Thode et al, 2019) are probably one of the most informative methods and may match the perceptual world of fishes best (Popper and Fay, 2011;Nedelec et al, 2016;Popper and Hawkins, 2018).…”