In shallow-water environments, source localization often suffers from reduced performance in conventional array signal processing techniques for frequency bands above 1 kHz due to environmental mismatch. A recently proposed technique, called the steered frequency–wavenumber (SFK) analysis method, overcomes this limitation. By incorporating beam-steering techniques into frequency–wavenumber analysis, this method enables target localization even in sparse conditions where high-frequency signals are received. This study extends the SFK method by applying various adaptive signal processing techniques, with a particular focus on the minimum-variance distortionless response and white noise gain constraint methods. Using snapping shrimp sounds from the SAVEX15 experiment, we analyzed localization performance and compared it with the Bartlett SFK approach. The snapping shrimp signals have frequency components ranging from 5 to 24 kHz and exhibit impulsive characteristics with a duration of 0.2 ms. Signals recorded by a sparse vertical array of 16 sensors, with a 60-m aperture in 100-m shallow water, enabled the localization of a source at a range of 38 m and a depth of 99.8 m.

This study proposes a TDOA-MLP method for estimating the range of snapping shrimp sources from the array-wide time-difference-of-arrival (TDOA) structure measured by a vertical line array. BELLHOP arrival-time replicas were generated for the SAVEX15 environment by varying source range, array tilt, and maximum water depth, and a multilayer perceptron was trained to estimate source range with array tilt as an auxiliary output. To provide a more stringent assessment of range generalization, six continuous source-range intervals were excluded from training, and only their inner portions were used for blocked-holdout evaluation, providing an approximately 2-m separation from the nearest training ranges. The resulting overall range-estimation mean absolute error (MAE) and root mean square error (RMSE) were 0.864 and 1.056 m, respectively, with regional MAEs of 0.762, 0.728, and 1.119 m for the near-, mid-, and far-range regions. Additional analyses showed that the estimation error increased with controlled arrival-time perturbations and varied with the maximum-depth condition. When applied to experimental SAVEX15 snapping shrimp data, the TDOA-MLP produced range distributions and snap-to-snap variations generally consistent with those obtained using template matching, steered frequency-wavenumber analysis, and frequency-difference matched-field processing. Relative to template matching, the mean absolute relative range difference was 4.54%. Because ground-truth source ranges were unavailable for the experimental data, the field results demonstrate consistency among the methods rather than absolute localization accuracy. The proposed method provides a low-dimensional learning-based approach for estimating the range of impulsive snapping shrimp signals from array-wide TDOA structures.

Passive acoustic monitoring (PAM) is a widely adopted methodology for the continuous, long-term observation of underwater marine organisms as well as various underwater noise sources using acoustic signals. However, manually identifying specific signals within long-term monitoring datasets is cumbersome and susceptible to potential biases. This study proposes a hybrid automated detection framework that simultaneously improves detection accuracy and operational efficiency for continuous 45-day underwater acoustic data on the grunt signals of Pacific cod (Gadus macrocephalus). The framework employs a theoretical kernel matching the dominant signal harmonic frequency components to rapidly filter potential candidates with minimal false negatives, followed by a class-weighted ResNet-18 to eliminate residual false positives arising from severe class imbalance in real-sea environments. To prevent data snooping, the classification probability threshold was systematically investigated on an independent validation dataset; freezing the canonical threshold at 0.5 effectively suppressed false alarms caused by ambient soundscapes. On the chronologically distinct test dataset, the proposed model achieved an outstanding and balanced classification performance, with 97.67% precision, 94.83% recall, and a 96.23% F1-score. These findings demonstrate that the integrated matched filter and deep learning pipeline provides a reliable automated approach for identifying biological fish sounds within complex marine acoustic environments.

In real ocean environments, multiple ship noises are often received simultaneously, making it difficult to accurately estimate the noise of a specific target ship. In particular, in multipath propagation environments, signal distortion caused by propagation paths further complicates the separation and reconstruction of the target signal. In this study, a hybrid replica-based adaptive time reversal processing (ATRP) method is proposed to reconstruct target ship noise in environments where multiple ship noises are overlapped. First, the Green's function of the propagation path is estimated from the received signals using blind deconvolution, and a hybrid replica is constructed based on the estimated result. The generated replica is then used to perform ATRP, enabling selective reconstruction of the target ship noise. The performance of the proposed method was verified through numerical simulations, and its applicability was examined using real ocean data. The reconstruction performance of ship noise was evaluated based on spectral characteristics and sound pressure levels. The results demonstrate that the proposed hybrid replica–based ATRP method can effectively reconstruct the main spectral characteristics of target ship noise even in environments with overlapping ship noises.