To achieve high-quality voice communication technology without noise interference in flammable, explosive and strong electromagnetic environments, the speech enhancement technology of a fiber-optic external Fabry–Perot interferometric (EFPI) acoustic sensor based on deep learning is studied in this paper. The combination of a complex-valued convolutional neural network and a long short-term memory (CV-CNN-LSTM) model is proposed for speech enhancement in the EFPI acoustic sensing system. Moreover, the 3 × 3 coupler algorithm is used to demodulate voice signals. Then, the short-time Fourier transform (STFT) spectrogram features of voice signals are divided into a training set and a test set. The training set is input into the established CV-CNN-LSTM model for model training, and the test set is input into the trained model for testing. The experimental findings reveal that the proposed CV-CNN-LSTM model demonstrates exceptional speech enhancement performance, boasting an average Perceptual Evaluation of Speech Quality (PESQ) score of 3.148. In comparison to the CV-CNN and CV-LSTM models, this innovative model achieves a remarkable PESQ score improvement of 9.7% and 11.4%, respectively. Furthermore, the average Short-Time Objective Intelligibility (STOI) score witnesses significant enhancements of 4.04 and 2.83 when contrasted with the CV-CNN and CV-LSTM models, respectively.
This paper presents a novel improvement in the optical path structure of a three-wavelength symmetric demodulation method applied to extrinsic Fabry–Perot interferometer (EFPI) fiber optic acoustic sensors. The traditional approach of using couplers to construct the phase difference in the symmetric demodulation method is replaced with a new approach that combines the symmetric demodulation algorithm with wavelength division multiplexing (WDM) technology. This improvement addresses the issue of a suboptimal coupler split ratio and phase difference, which can affect the accuracy and performance of the symmetric demodulation method. In an anechoic chamber test environment, the symmetric demodulation algorithm implemented with the WDM optical path structure achieved a signal-to-noise ratio (SNR) of 75.5 dB (1 kHz), a sensitivity of 1104.9 mV/Pa (1 kHz), and a linear fitting coefficient of 0.9946. In contrast, the symmetric demodulation algorithm implemented with the traditional coupler-based optical path structure achieved an SNR of 65.1 dB (1 kHz), a sensitivity of 891.75 mV/Pa (1 kHz), and a linear fitting coefficient of 0.9905. The test results clearly indicate that the improved optical path structure based on WDM technology outperforms the traditional coupler-based optical path structure in terms of sensitivity, SNR, and linearity.
To establish stable communication networks in harsh environments where power supply is difficult, such as coal mines and underwater, we propose an effective scheme for co-transmission of analog audio signals and energy. By leveraging the advantages of optical fibers, such as corrosion resistance and strong resistance to electromagnetic interference, the scheme uses a 1550 nm laser beam as the carrier for analog audio signal propagation, which is then converted to electrical energy through a custom InGaAs/InP photovoltaic power converter (PPC) for energy supply and information transfer without an external power supply after a 25 km fiber transmission. In the experiment, with 160 mW of optical power injection, the scheme not only provides 4 mW of electrical power, but also transmits an analog signal with an acoustic overload point (AOP) of 105 dBSPL and a signal-to-noise ratio (SNR) of 50 dB. In addition, the system employs wavelength division multiplexing (WDM) technology to transform from single-channel to multi-channel communication on a single independent fiber, enabling the arraying of receiving terminals. The passive arrayed terminals make the multi-channel long-distance audio transmission system using power-over-fiber (PoF) technology a superior choice for establishing a stable communication network in harsh environments.
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