Data-Driven Approach For Acoustic Source Localization
Résumé
The spatial and temporal characteristics of the dominant hydrodynamic contributors to far-field aeroacoustic noise are identified through a data-driven approach. This approach is proposed as an alternative to traditional acoustic source localization methods such as beamforming which suffer from poor resolution at lower source frequencies. A training database was developed using analytically-defined monopoles as static and moving acoustic sources. These sources were randomly distributed over a scanning grid of fixed size and a microphone array was simulated to gather information about the sources in the form of the Cross-Spectral Matrix (CSM). The CSM was used as an input feature to train a Convolutional Neural Network (CNN) to identify position, strength, and velocity of the sources with far greater accuracy than traditional methods. Citing a particular result, the network could detect all the 6 sources at a frequency of 8000 Hz spread randomly over a 12x12 grid with just a handful of training (100 epochs). The performance was equally good for lower frequencies. Networks built for more realistic cases such as detecting sources with different strengths, or, detecting a random number of sources also yielded promising results. This approach is then used to identify acoustic noise sources in simplified vortex pairing.Fichiers supplémentaires
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