Study of acoustic propagation across an oceanic front at the edge of the New England shelf.

Propagation of low-frequency sound across a warm core ring-enhanced oceanic front at lower horizontal grazing angles is presented. The data were collected from an experimental source tow conducted during the New England Shelf Break Acoustics (NESBA) experiments in spring 2021. The 3 h tow track provides spatiotemporal measurements of acoustic propagation through the front across varying geometries.

Real-time joint ocean acoustics and circulation modeling in the 2021 New England Shelf Break Acoustics experiment (L).

During the spring of 2021, a coordinated multi-vessel effort was organized to study physical oceanography, marine geology and biology, and acoustics on the northeast United States continental shelf, as part of the New England Shelf Break Acoustics (NESBA) experiment. One scientific goal was to establish a real-time numerical model aboard the research vessel with high spatial and temporal resolution to predict the oceanography and sound propagation within the NESBA study area. The real-time forecast model performance and challenges are reported in this letter without adjustment or re-simulation after the cruise.

Deep embedded clustering of coral reef bioacoustics.

Deep clustering was applied to unlabeled, automatically detected signals in a coral reef soundscape to distinguish fish pulse calls from segments of whale song. Deep embedded clustering (DEC) learned latent features and formed classification clusters using fixed-length power spectrograms of the signals. Handpicked spectral and temporal features were also extracted and clustered with Gaussian mixture models (GMM) and conventional clustering.

Automated two-dimensional localization of underwater acoustic transient impulses using vector sensor image processing (vector sensor localization).

Detecting acoustic transients by signal-to-noise ratio (SNR) becomes problematic in nonstationary ambient noise environments characteristic of coral reefs. An alternate approach presented here uses signal directionality to automatically detect and localize transient impulsive sounds collected on underwater vector sensors spaced tens of meters apart. The procedure, which does not require precise time synchronization, first constructs time-frequency representations of both the squared acoustic pressure (spectrogram) and dominant directionality of the active intensity (azigram) on each sensor.

Block sparse Bayesian learning for broadband mode extraction in shallow water from a vertical array.

The horizontal wavenumbers and modal depth functions are estimated by block sparse Bayesian learning (BSBL) for broadband signals received by a vertical line array in shallow-water waveguides. The dictionary matrix consists of multi-frequency modal depth functions derived from shooting methods given a large set of hypothetical horizontal wavenumbers. The dispersion relation for multi-frequency horizontal wavenumbers is also taken into account to generate the dictionary.

A feedforward neural network for direction-of-arrival estimation.

This paper examines the relationship between conventional beamforming and linear supervised learning, then develops a nonlinear deep feed-forward neural network (FNN) for direction-of-arrival (DOA) estimation. First, conventional beamforming is reformulated as a real-valued, linear inverse problem in the weight space, which is compared to a support vector machine and a linear FNN model. In the linear formulation, DOA is quickly and accurately estimated for a realistic array calibration example.

Machine learning in acoustics: Theory and applications.

Acoustic data provide scientific and engineering insights in fields ranging from biology and communications to ocean and Earth science. We survey the recent advances and transformative potential of machine learning (ML), including deep learning, in the field of acoustics. ML is a broad family of techniques, which are often based in statistics, for automatically detecting and utilizing patterns in data.

Deep-learning source localization using multi-frequency magnitude-only data.

A deep learning approach based on big data is proposed to locate broadband acoustic sources using a single hydrophone in ocean waveguides with uncertain bottom parameters. Several 50-layer residual neural networks, trained on a huge number of sound field replicas generated by an acoustic propagation model, are used to handle the bottom uncertainty in source localization. A two-step training strategy is presented to improve the training of the deep models.

Ship localization in Santa Barbara Channel using machine learning classifiers.

Machine learning classifiers are shown to outperform conventional matched field processing for a deep water (600 m depth) ocean acoustic-based ship range estimation problem in the Santa Barbara Channel Experiment when limited environmental information is known. Recordings of three different ships of opportunity on a vertical array were used as training and test data for the feed-forward neural network and support vector machine classifiers, demonstrating the feasibility of machine learning methods to locate unseen sources. The classifiers perform well up to 10 km range whereas the conventional matched field processing fails at about 4 km range without accurate environmental information.

Eastern Arctic ambient noise on a drifting vertical array.

Ambient noise in the eastern Arctic was studied from April to September 2013 using a 22 element vertical hydrophone array as it drifted from near the North Pole (89° 23'N, 62° 35'W) to north of Fram Strait (83° 45'N, 4° 28'W). The hydrophones recorded for 108 min/day on six days per week with a sampling rate of 1953.125 Hz.