Audio Signal Extraction and Enhancement Based on CNN From Laser Speckles

Liu, Chang; Li, Lin; Huang, Xueyuan; Wang, Xiaozhong; Wang, Cheng

doi:10.1109/jphot.2021.3136908

Cited by 5 publications

(4 citation statements)

References 21 publications

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“…Ncorr [ 20 ] is a digital correlation algorithm specially designed by researchers for two-dimensional images. In recent years, with the rapid development of machine learning and artificial intelligence technology, many neural-network-based methods have been proposed, such as convolutional neural network (CNN) [ 30 , 31 , 32 ] and convolutional long short-term memory (LSTM) [ 33 ].…”

Section: Introductionmentioning

confidence: 99%

A Speech Enhancement Algorithm for Speech Reconstruction Based on Laser Speckle Images

Hao

Zhu

Wang

et al. 2022

Sensors

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In the optical system for reconstructing speech signals based on laser speckle images, the resonance between the sound source and nearby objects leads to frequency response problem, which seriously affects the accuracy of reconstructed speech. In this paper, we propose a speech enhancement algorithm to reduce the frequency response. The results show that after using the speech enhancement algorithm, the frequency spectrum correlation coefficient between the reconstructed sinusoidal signal and the original sinusoidal signal is improved by up to 82.45%, and the real speech signal is improved by up to 56.40%. This proves that the speech enhancement algorithm is a valuable tool for solving the frequency response problem and improving the accuracy of reconstructed speech.

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Section: Introductionmentioning

confidence: 99%

A Speech Enhancement Algorithm for Speech Reconstruction Based on Laser Speckle Images

Hao

Zhu

Wang

et al. 2022

Sensors

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“…In order to detect high frequency information, according Nyquist sampling theory, a high-frame rate camera is required which limits the maximum detectable frequency of the vibrating speckle pattern. To overcome this problem, several groups have turned to line array cameras [22][23][24][25][26][27][28], and single pixel detector methods [29] which can achieve higher sampling frequencies. The main drawback of these methods, however, are the difficulty of the alignment process making them unpractical.…”

Section: Introductionmentioning

confidence: 99%

Rolling-shutter laser speckle analysis in bio-photonics

Apsel,

Tarle,

Yemini

et al. 2024

Biomedical Spectroscopy, Microscopy, and Imaging III

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In this paper, an innovative approach for detecting and analyzing speckle pattern signals is demonstrated, based on dynamic speckle analysis using a low-cost and low-framerate rolling shutter (RS) CMOS image sensor. The row scanning mechanism of a rolling shutter camera samples dynamic speckle patterns at a higher rate than typical Global Shutter (GS) cameras. In this research we demonstrate the detection and analysis of vibration signals that arise from an acoustic signal. We will illustrate the process of reconstructing a voice signal by analyzing a vibrating speckle pattern, with a primary focus on detecting and audibly capturing lung sounds.

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“…Compared with ANN, convolutional neural network (CNN) is a special neural network that introduces convolution and pooling operations to generate deep features, thereby enhancing the discrimination and reducing the computational cost. CNN has been applied to signal extraction [26] and analysis [27] in photonics, which is faster and more accurate than traditional methods. The shaping current technique can be mapped by the trained CNN and further save the computing time of the control equation when realizing the parameter optimization.…”

Section: Introductionmentioning

confidence: 99%

Suppressing DFB Lasers Relaxation Oscillations by High-Order Continuous Shaping Current With CNN Prediction

Ding

Liu

et al. 2023

IEEE J. Quantum Electron.

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A technique of continuous shaping current waveform to suppress relaxation oscillations (ROs) of distributed feedback (DFB) laser for a high-performance optic system is demonstrated. To effectively suppress ROs, expressions for the shaping current waveform are theoretically derived based on the rate equations and different polynomials for the 3 rd , 5 th , and 8 th order Fourier basis functions are introduced. The convolutional neural network (CNN) is employed to predict the multi-parameter values that determine the results of the shaping input current, which exempt from the difficult and time-consuming process of parameter selection. Prior to training, preprocessing of the data obtained from DFB laser forward simulation using min-max normalization aims to improve the training efficiency of the CNN. The shaping current signals obtained from the CNN predicted parameters are put into the equivalent circuit model for the DFB laser to verify the effectiveness of the shaping current technique and CNN parameter optimization. Afterwards, the shaping current waveform is verified in a time division multiplex passive optical network (TDM-PON) utilizing the DFB laser model as a directly modulated source achieving remarkable performance with low cost. The results show that the high-order continuous shaping current modulated technique can successfully suppress the ROs and enhance the performance of the optic system.

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Audio Signal Extraction and Enhancement Based on CNN From Laser Speckles

Cited by 5 publications

References 21 publications

A Speech Enhancement Algorithm for Speech Reconstruction Based on Laser Speckle Images

A Speech Enhancement Algorithm for Speech Reconstruction Based on Laser Speckle Images

Rolling-shutter laser speckle analysis in bio-photonics

Suppressing DFB Lasers Relaxation Oscillations by High-Order Continuous Shaping Current With CNN Prediction

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