Audio Signal Extraction and Enhancement Based on CNN From Laser Speckles

A micro vibration signal extraction method based on deep neural network is proposed. Rough surface of vibrating object modulates the illuminating laser wave front and generates speckle pattern, which is recorded by a linear array CMOS and preprocessed and input into a 16-layer convolution neural network (CNN) trained with specially prepared data. The optical experimental setup is analyzed to fulfil the temporal and spatial Shannon sampling theorem. The output audio signals are evaluated with standard algorithms and show enhanced segmental SNR and intelligibility. The effects of different input audio types and quality of raw audio signals are investigated, and the results show that the neural network is robust to the input. The CNN structure is optimized and the results show the performance decrease with the reduction of convolution layers. The performances of three popular deep neural networks are compared and the performance of CNN is better.