Deep Learning-Based Speech Enhancement of an Extrinsic Fabry-Perot Interferometric Fiber Acoustic Sensor System

• Published in: Sensors (Basel, Switzerland) Vol. 23; no. 7; p. 3574
• Main Authors: Chai, Shiyi, Guo, Can, Guan, Chenggang, Fang, Li
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.03.2023; MDPI
• Subjects: Acoustics; Algorithms; CV-CNN; CV-LSTM; Deep learning; Electromagnetism; Equipment and supplies; external Fabry–Perot interferometer; Extrinsic Fabry-Perot interferometers; Fiber optics; Fourier transforms; Intelligibility; Interferometry; Machine learning; Methods; Model testing; Neural networks; Noise; optical fiber sensor; Sensors; Silicon nitride; Speech; speech enhancement; Speech processing; Support vector machines; Test sets; external Fabry–Perot interferometer; CV-LSTM; optical fiber sensor; speech enhancement; CV-CNN
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s23073574

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.