Remote recovery of audio signals from videos of optical speckle patterns: a comparative study of signal recovery algorithms

Abstract

Optical remote sensors are nowadays ubiquitously used, thanks to unprecedented advances in the last decade in photonics, machine learning and signal processing tools. In this work we study experimentally the remote recovery of audio signals from the silent videos of the movement of optical speckle patterns. This technique can be used even when in between the source and the receiver there is a medium that does not allow for the propagation of sound waves. We use a diode laser to generate a speckle pattern on the membrane of a loudspeaker and a low-cost CCD camera to record the video of the movement of the speckle pattern when the loudspeaker plays an audio signal. We perform a comparative analysis of six signal recovery algorithms. In spite of having different complexity and computational requirements, we find that the algorithms have (except for the simplest one) good performance in terms of the quality of the recovered signal. The best trade-off, in terms of computational costs and performance, is obtained with a new method that we propose, which recovers the signal from the weighted sum of the intensities of all the pixels, where the signs of the weights are determined by selecting a reference pixel and calculating the signs of the cross-correlations of the intensity of the reference pixel and the intensities of the other pixels.

Audio Signal Extraction and Enhancement Based on CNN From Laser Speckles

“…To extract audio signals from optical speckle patterns, the principle signal processing algorithms include cross-correlation, flux variations, optical flow, channel fusion, complex pyramid [13], and machine learning. Barcellona et al compared the commonly used algorithms and concluded that the machine learning method obtained the best performance [14]. However, detailed investigation of machine learning method is not introduced in that paper.…”

The 20k Samples-Per-Second Real Time Detection of Acoustic Vibration Based on Displacement Estimation of One-Dimensional Laser Speckle Images

“…For example, the requirement for sampling human speech is at least 8 kHz. The real time sampling rate of previous methods cannot satisfy this requirement [ 26 ].…”

Experimental study of speckle patterns generated by low-coherence semiconductor laser light

“…12 Different approaches for extracting meaningful information from speckle patterns have been proposed. [13][14][15] In addition, the intensity distribution, the speckle spot size, and speckle correlations provide information of the spectral properties of the source that generates the light. 16 High-precision spectrometers have been demonstrated, where wavelength-dependent speckle patterns are detected and, after calibration, used to recover the spectrum of the light.…”

“…For example, speckle has been used for remote vibration analysis and the remote recovery of audio signals 12 . Different approaches for extracting meaningful infor-arXiv:2006.02719v1 [physics.optics] 4 Jun 2020 mation from speckle patterns have been proposed [13][14][15] .…”