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Broadband spectrograms from surface ships are employed in convolutional neural networks (CNNs) to predict the seabed type, ship speed, and closest point of approach (CPA) range. Three CNN architectures of differing size and depth are trained on different representations of the spectrograms. Multitask learning is employed; the seabed type prediction comes from classification, and the ship speed and CPA range are estimated via regression. Due to the lack of labeled field data, the CNNs are trained on synthetic data generated using measured sound speed profiles, four seabed types, and a random distribution of source parameters. Additional synthetic datasets are used to evaluate the ability of the trained CNNs to interpolate and extrapolate source parameters. The trained models are then applied to a measured data sample from the 2017 Seabed Characterization Experiment (SBCEX 2017). While the largest network provides slightly more accurate predictions on tests with synthetic data, the smallest network generalized better to the measured data sample. With regard to the input data type, complex pressure spectral values gave the most accurate and consistent results for the ship speed and CPA predictions with the smallest network, whereas using absolute values of the pressure provided more accurate results compared to the expected seabed types.

In ocean acoustics, many types of optimizations have been employed to locate acoustic sources and estimate the properties of the seabed. How these tasks can take advantage of recent advances in deep learning remains as open questions, especially due to the lack of labeled field data. In this work, a Convolutional Neural Network (CNN) is used to find seabed type and source range simultaneously from 1 s pressure time series from impulsive sounds. Simulated data are used to train the CNN before application to signals from a single hydrophone signal during the 2017 Seabed Characterization Experiment. The training data includes four seabeds representing deep mud, mud over sand, sandy silt, and sand, and a wide range of source parameters. When applied to measured data, the trained CNN predicts expected seabed types and obtains ranges within 0.5 km when the source-receiver range is greater than 5 km, showing the potential for such algorithms to address these problems.

A broadband equivalent acoustic source distribution can be used to model the sound field near a high-speed jet. Such models must account for the spatiospectral variation of the sound levels. This work presents a technique for obtaining such a model using a spectral decomposition method associated with large and fine-scale turbulent mixing noise to create broadband equivalent source distributions for each noise type. The large-scale turbulent mixing noise is represented by frequency-dependent wavepackets, while the fine-scale turbulent mixing noise is modeled as a frequency-dependent incoherent, extended source distribution. This technique is applied to acoustical measurements from an ideally expanded, unheated Mach 1.8 jet. The wavepackets model the sound field levels in the maximum radiation region, but the second incoherent source distribution is required to obtain the levels at the other locations. The combination of the incoherent source distribution and the wavepacket provides a broadband, equivalent acoustic source representation that adequately models the sound field for Strouhal numbers between 0.04 and 0.25. At higher Strouhal numbers, better agreement is obtained when accounting for a frequency-dependent shift in the apparent acoustic source region. This frequency-dependent source region is more important closer to the jet than in the far field.

A risk factor shared by all musicians is a potential for noise-induced hearing loss. A study was conducted to explore what factors determine noise dosage during musical events experienced by college student musicians. First, noise exposure during specific activities was explored, including during ensemble rehearsal and personal practice. Next, full-day noise exposure was investigated by measuring levels experienced by student musicians during a typical daily routine. Factors such as instrument played, type of activity, location within ensemble, and room environment were related to noise dosage. Disparities in results using different standards to calculate noise dosage were also explored. Risk of noise overexposure was found to be greater in some instrument classes, such as wind instruments, than in others, such as string instruments, and can yield vastly different results depending on the metric used for noise dosage calculation.

Broadband shock-associated noise (BBSAN) is a prominent noise component from nonideally expanded jets in the forward and sideline directions. BBSAN from laboratory-scale jets has been studied extensively, and spatial trends in BBSAN spectral peak characteristics-frequency, level, and width-have been established. These laboratory-scale trends are compared to those for BBSAN from a tied-down F-35B operated at four engine conditions. While the peak frequency varies as expected, both spatially and across engine condition, the peak level and width do not, pointing to the need for additional research into BBSAN for high-performance military aircraft.