Analysis of shielding of propeller noise using beamforming and predictions.

Engine noise shielding is an important measure towards low-noise aircraft configurations. Such designs are supported by prediction tools that indicate high values for shielding of engine noise. Most prediction models approximate the complex nature of engine noise to simple noise sources such as monopoles or dipoles. This work compares predictions of noise shielding with experiments using different noise sources and shielding body geometries. The experiments considered in this work concern a monopole source shielded by a flat plate and a NACA 64-008 A wing, and a propeller shielded by the same wing. Comparisons between models and measurements are made by analysis of noise levels at individual microphones and using conventional beamforming. Results show that for the monopole cases the model predictions are in agreement with the experimental data, with an average deviation of 2-3 dB. The curvature of the leading edge of the wing influences the noise shielding results. The measured values of noise shielding of propeller noise are lower than those measured for the omni-directional source. Different types of source directivity are used to approximate the propeller in the predictions: monopole, dipole and a multi-source. The dipole approximation shows the best agreement with the experiments for the case of the propeller.

On the use of global optimization methods for acoustic source mapping.

Conventional beamforming with a microphone array is a well-established method for localizing and quantifying sound sources. It provides estimates for the source strengths on a predefined grid by determining the agreement between the pressures measured and those modeled for a source located at the grid point under consideration. As such, conventional beamforming can be seen as an exhaustive search for those locations that provide a maximum match between measured and modeled pressures. In this contribution, the authors propose to, instead of the exhaustive search, use an efficient global optimization method to search for the source locations that maximize the agreement between model and measurement. Advantages are two-fold. First, the efficient optimization allows for inclusion of more unknowns, such as the source position in three-dimensional or environmental parameters such as the speed of sound. Second, the model for the received pressure field can be readily adapted to reflect, for example, the presence of more sound sources or environmental parameters that affect the received signals. For the work considered, the global optimization method, Differential Evolution, is selected. Results with simulated and experimental data show that sources can be accurately identified, including the distance from the source to the array.

Common source identification of images in large databases.

Photo-response non-uniformity noise patterns are a robust way to identify the source of an image. However, identifying a common source of images in a large database may be impractical due to long computation times. In this paper a solution for large volume digital camera identification is proposed, which combines, and sometimes slightly modifies, existing methods for a 500 times improvement in the speed of common source identification. Single image comparisons are often plagued by considerable noise contamination from scene content and random noise, which makes it harder to accomplish reliable common source identification. Therefore a new method is introduced that can increase true positive rates by more than 45% at very low computation costs. Analysis of real data from a fraud case shows the effectiveness of the proposed method. As a whole the proposed solution makes it possible to analyze a large database in forensically relevant time, without resorting to large and expensive computer clusters.