Source depth estimation with a vertical line array generally involves mode filtering, then matched-mode processing. Because mode filtering is an ill-posed problem if the water column is not well-sampled, concerns for robustness motivate a simpler approach: source depth discrimination considered as a binary classification problem. It aims to evaluate whether the source is near the surface or submerged. These two hypotheses are formulated in terms of normal modes, using the concept of trapped and free modes. Decision metrics based on classic mode filters are proposed. Monte Carlo methods are used to predict performance and set the parameters of a classifier accordingly.

An approach for the estimation of single reed parameters during playing, using an instrumented mouthpiece and an iterative method, is presented. Different physical models describing the reed tip movement are tested in the estimation method. The uncertainties of the sensors installed on the mouthpiece and the limits of the estimation method are studied. A tenor saxophone reed is mounted on this mouthpiece connected to a cylinder, played by a musician, and characterized at different dynamic levels. Results show that the method can be used to estimate the reed parameters with a small error for low and medium sound levels (piano and mezzoforte dynamic levels). The analysis reveals that the complexity of the physical model describing the reed behavior must increase with dynamic levels. For medium level dynamics, the most relevant physical model assumes that the reed is an oscillator with non-linear stiffness and damping, the effect of mass (inertia) being very small.

The problem of acoustic source depth discrimination was introduced as a way to get basic information on source depth in configurations where accurate depth estimation is not feasible. It is a binary classification problem, aiming to evaluate whether the source is near the surface or submerged. Herein, the classification relies on a signal measured with a horizontal line array in shallow water. Knowing the source-array distance is not required but the source bearing has to be close to the array endfire. Signal processing relies on a normal-mode propagation model, and thus requires prior knowledge of the mode characteristics. The decision relies on an estimation of the trapped energy ratio in mode space. The performance is predicted with simulations and Monte Carlo methods, allowing one to compare several estimators based on different mode filters, and to choose an appropriate decision threshold. The impact on performance of frequency, noise level, horizontal aperture, and environmental mismatch is numerically studied. Finally, the approach is validated on experimental data acquired with a horizontal line array deployed off the coast of New Jersey, and the impact of errors in the environmental model is illustrated. The investigated approach successfully identifies a surface ship and a submerged towed source.

International audienceSource depth estimation with a vertical linear array generally involves mode filtering, followed by matched-mode processing. However, this method has two main limitations: the problem of mode filtering is ill-posed in the case of partially spanning arrays; matched-mode processing is sensitive to environmental mismatch. Therefore, concerns for robustness motivate a simpler approach. The problem of depth estimation is reduced to a binary classification problem: source depth discrimination. Its aim is to evaluate whether the source is near the surface or submerged. These two hypotheses are formulated in terms of normal modes, using the concept of trapped and free modes. Several classification rules, based on modal filtering or on subspace projections, are studied. Monte-Carlo methods are used to evaluate their performance and compute receiver operating characteristics. This allows the choice of a discrimination threshold according to some expected performance. The benefits of considering a source depth discrimination problem rather than a source localization one are highlighted. The influence of noise and environmental mismatch are investigated, as well as the choice of the discrimination depth and the choice of the limit between trapped and free modes