DEEP NEURAL NETWORK MODELS FOR ACOUSTIC SOURCE LOCALIZATION
The localization of acoustic sources using acoustic imaging methods such as acoustic beamforming can be limited by the Rayleigh resolution limit at relatively low frequencies and the output of these methods may also produce spatial aliasing images referred to as sidelobes, particularly at high frequencies. To date, there are very few Deep Neural Network (DNN) applications for acoustic imaging to help alleviate some of these issues. In this study, we developed several DNN models using DenseNet 201 with a training strategy specifically designed for an acoustic imaging task. The DNN method is able to recognize the pattern behind microphone array signals for different source positions, by using the real component of the cross spectral matrix of the received pressure vectors at the microphone positions. DNN models with both a fixed and random number of input sources are simulated for a range of specific frequencies. The DNN model with a random number of input sources is tested against conventional beamforming, CLEAN SC and DAMAS, revealing a far improved source localization and source strength estimation. These DNN models represent a very promising proof of concept for the use of DNN models in the field of acoustic imaging.