Artificial Intelligence-Driven Signal Distortion Compensation Method for Fiber-Optic Microphones

Le résumé fourni par la source

This paper addresses the signal distortion issues in interferometric fiber-optic sensors caused by factors such as non-flat probe frequency response, insufficient detection circuit bandwidth, and imperfect demodulation algorithms. To mitigate these distortions, we introduce a deep neural network based on the Dual-Path Convolution Recurrent Network (DPCRN). The Scale-Invariant Signal-to-Distortion Ratio (SI-SDR) is employed as the loss function, replacing the conventional Mean Squared Error (MSE) in the frequency domain. A dataset consisting of 8,000 10-second audio segments was constructed for training and validation, resulting in a well-trained DPCRN model. Experimental results demonstrate improvements, with the Short-Time Objective Intelligibility (STOI) score increasing by 0.58, the Perceptual Evaluation of Speech Quality (PESQ) score improving by 0.059, and the Signal-to-Distortion Ratio (SDR) increasing by 8.13 dB. These results highlight the effectiveness of the proposed method in compensating for signal distortion in fiber-optic microphones and show promising potential in enhancing the consistency of fiber-optic microphone arrays.

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