Investigating the Role of Compact Neural Networks in Improving Near-Field Beamforming Techniques

The point of this study is to investigate how small neural networks help build near-field beamforming methods. This investigation will be done, among other things. One of the requirements for this study is to take this test. The suggested way for locating sound sources in the near field could lower the frequency of spatial aliasing and improve the accuracy of estimating the direction of arrival (DOA). Both benefits can be yours if you use the plan. When deep learning is used, this becomes a possible and attainable goal. It is shown that the suggested method is superior to the usual beamforming methods by a full comparison with these six kinds of methods. This study is going to be done to show that the planned method is better. Scatter plots are used to show the data clearly and draw attention to the possible benefits of the new method.