# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
###### Abstract  
Laser speech measurement is a new sound capture technology based on Laser Doppler Vyrometry (LDV). It avoids the need for contact, is easily concealed and is ideal for remote speech acquisition, which has led to its wide-scale adoption for military and security applications. However, lasers are easily affected by complex detection environments. Thus, speckle noise often appears in the measured speech, seriously affecting its quality and intelligibility. This paper examines all of the characteristics of impulsive noise in laser measured speech and proposes a novel automatic impulsive noise detection and removal method. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the noise-to-signal ratio (NSR) of the measured signal. This makes it possible to detect the position of noise through a combination of the average short-time energy and kurtosis. The method not only precisely locates small clicks (with a duration of just a few samples), but also finds the location of longer bursts and scratches (with a duration of up to a hundred samples). The located samples can then be replaced by more appropriate samples whose coding is based on the LP model. This strategy avoids unnecessary processing and obviates the need to compromise the quality of the relatively large fraction of samples that are unaffected by speckle noise. Experimental results show that the proposed automatic speckle noise detection and removal method outperforms other related methods across a wide range of degraded audio signals. l acr 2021 19 October 2  
signal and the coherent signal. After demodulation, filtering and some signal processing steps are performed to obtain the voice signal [1]. As laser speech detection systems use lasers for voice measurement, they can detect speech in non-contact situations, undertake long\(-\)range measurements and are easy to conceal and operate [2]. Laser speech detection systems are usually set up in hidden places to detect conversations in conference rooms, cars, etc. Figure 2 shows a scene where a laser speech detection system is actually being used, with the objects near the speaker (such as the computer screens, tissue box, mineral water bottle, clothes, etc.) being used as sound sensors. As the sound signal is captured through the indirect measurement of vibrations, the selection of the objects for detection has a significant impact on the speech acquisition, as does the external environment. First of all, the surface of most detected objects is extremely rough in the laser band and scattered light is therefore emitted from numerous coherent points. These scattered sources propagate in different directions and can interfere with each other in space, resulting in a random distribution of light interference and generating what is known as speckle noise (in speech, it shows clicks and small burrs as shown in the black box in Figure 3). In addition, when the measured light is affected by interference (such as people walking, environmental occlusion, violent shaking, atmospheric turbulence, etc. ), it not only makes it difficult for the measurement light to focus on the surface of the detection beacon, but also causes wavefront phase distortion in the reflected light, resulting in destructive interference. This makes the Doppler phase obtained by the detection system discontinuous between \(-\pi\) and \(\pi\)[3]. These discontinuities lead to further kinds of speckle noise in the speech signal (such as bursts, outliers, crackles, scrapes, etc. ), as shown in the red box in Figure 3.  
Figure 1: Internal structure of a laser speech measurement system. Figure 2: Real example of detection by a laser speech measurement system. In speech, speckle noise is in the form of impulsive noise, which seriously reduces the quality and intelligibility of measured speech and has a significant impact on its viability for speech intelligence.