Otosclerosis is a common disease of the middle ear leading to stapedial fixation. Its rapid and non-invasive diagnosis could be achieved through wideband tympanometry (WBT), but the interpretation of the raw data provided by this tool is complex and time-consuming. Convolutional neural networks (CNN) could potentially be applied to this situation to help the clinicians categorize WBT data. A dataset containing 135 samples from 80 patients with otosclerosis and 55 controls was obtained. We designed a lightweight CNN to categorize samples into the otosclerosis and control. Receiver operating characteristic (ROC) analysis showed an area under the curve (AUC) of $0.95 \pm 0.011$, and the F1-score was $0.89 \pm 0.031$ ($r=10$). The performance was further improved by data augmentation schemes and transfer learning strategies (AUC: $0.97 \pm 0.010$, F1-score: $0.94 \pm 0.016$, $p< 0.05$, ANOVA). Finally, the most relevant diagnostic features employed by the CNN were assessed via the activation pattern heatmaps. These results are crucial for the visual interpretation of WBT graphic outputs which clinicians use in routine, and for a better understanding of the WBT signal in relation to the ossicular mechanics.

For identifying each vessel from ship-radiated noises with only a very limited number of data samples available, an approach based on the contrastive learning was proposed. The input was sample pairs in the training, and the parameters of the models were optimized by maximizing the similarity of sample pairs from the same vessel and minimizing that from different vessels. In practical inference, the method calculated the distance between the features of testing samples and those of registration templates and assigned the testing sample into the closest templates for it to achieve the parameter-free classification. Experimental results on different sea-trial data demonstrated the advantages of the proposed method. On the five-ship identification task based on the open-source data, the proposed method achieved an accuracy of $0.68$ when only five samples per vessel were available, that was significantly higher than conventional solutions with accuracies of $0.26$ and $0.48$. Furthermore, the convergence of the method and the behavior of its performance with increasing data samples available for the training were discussed empirically.

Bearing time record (BTR) is widely used in the field of passive sonar information processing for target detecting and tracking. One of its challenges is to obtain high-resolution beamforming power spectral to facilitate the directions estimating of arrivals, namely DOA estimating. This paper proposes a new algorithm framework Block Matching - Subband Extrema Energy Detection (BM-SEED) to supplement the resolution and enhance the weak targets of BTRs. It extracts the peak features of target trajectories within the conventional beamforming (CBF)-based BTRs by using an improved sub-band peak energy detection (SPED) algorithm, and then enhances the weak targets and suppresses the false-alarm targets through time-spatial autocorrelation analysis. Simulations and sea-trial data evaluations demonstrate that the method of this paper can enhance weak targets, eliminate the interferences of false targets and improve the resolution of BTR targets to 1 degree under −20 dB.

Digital beamforming techniques find wide applications in the field of underwater acoustic array signal processing. However, their azimuthal resolution has long been constrained by the Rayleigh limit, consequently limiting their detection performance. In this paper, we propose a novel two-dimensional Hessian–sparse deconvolution algorithm based on image processing techniques. This method assumes a priori that the underwater acoustic bearing time record (BTR) images exhibit sparsity, and then it first constructs partial differential equations in the beamforming domain with sparsity-norm constraints for optimal noise reduction. Subsequently, a two-dimensional deconvolution operation is applied to narrow the main lobe, aiming to achieve additional temporal gains in two-dimensional processing. The simulation and real sea trial data processing results show that the main lobe width of the proposed method is about 1.3 degrees at 0 dB. It effectively reduces the main lobe width and enhances the detection resolution of BTRs in the post-processing part, especially in low-signal-to-noise-ratio (SNR) environments. Therefore, the proposed method provides nice opportunities to further improve the target-detecting ability of hydrophone arrays.

Otosclerosis is a common middle ear disease that requires a combination of examinations for its diagnosis in routine. In a previous study, we showed that this disease could be potentially diagnosed by wideband tympanometry (WBT) coupled with a convolutional neural network (CNN) in a rapid and non-invasive manner. We showed that deep transfer learning with data augmentation could be applied successfully on such a task. However, the involved synthetic and realistic data have a significant discrepancy that impedes the performance of transfer learning. To address this issue, a Gaussian processes-guided domain adaptation (GPGDA) algorithm was developed. It leveraged both the loss about the distribution distance calculated by the Gaussian processes and the loss of conventional cross entropy during the transferring. On a WBT dataset including 80 otosclerosis and 55 control samples, it achieved an area-under-the-curve of $97.9\pm 1.1$ percent after receiver operating characteristic analysis and an F1-score of $95.7\pm 0.9$ percent that were superior to the baseline methods ($r=10$, $p<0.05$, ANOVA). To understand the algorithm’s behavior, the role of each component in the GPGDA was experimentally explored on the dataset. In conclusion, our GPGDA algorithm appears to be an effective tool to enhance CNN-based WBT classification in otosclerosis using just a limited number of realistic data samples.

Automatic classification of underwater soundscapes remains an open problem, mainly due to the challenges posed by multi-source interferences. Accurately classifying anthropogenic sounds in the marine environment, specifically those emanating from surface-ships, is concerned in this study. To achieve this, the convolutional neural network (CNN) with sine-cardinal-likeconstrained convolutional kernels is proposed, where kernels represent unsolved filter coefficients. Raw signals of sound pressures passively received by the hydrophone are used directly in this method without the need for the routine time-frequency analysis (TFA) beforehand. The convolutional layer with constrained kernels plays a crucial role in extracting features from hydroacoustic signals, effectively acting as a learnable extension of the bandpass filter groups with flexible bandwidth. One significant advantage of the proposed approach is its adaptive filtering capability based on the real-world data, which makes it effectively filter out irrelevant interference sounds. Such heterogeneous interferences often exhibit diverse unknown spectral ranges, making them challenging for conventional TFA with fixed parameters. By leveraging this adaptability, the output of the convolutional layer serves as a task-specific spectrogram, customized to the classifier after being trained on hydroacoustic data. The experiments on the ShipsEar dataset demonstrate the promising results of our solution than TFA and vanilla CNN.