The effect of speckles noise on the Laser Doppler Vibrometry for remote speech detection

Lv, Tao; Han, Xiyu; Wu, Shisong; Li, Yuanyang

doi:10.1016/j.optcom.2019.02.014

Cited by 26 publications

(10 citation statements)

References 19 publications

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“…The methods we chose for comparison are Cristalli and Lv's kurtosis ratio (KR)-based method [16,17], and Oudre's Autoregressive (AR)-based speckle noise detection and interpolation method [19,20]. In order to ensure consistency, LPC coded samples were used for replacement by all of the methods to obtain the final enhanced speech.…”

Section: Related Methodsmentioning

confidence: 99%

“…Outside of the above-mentioned, relatively straightforward time-domain and frequency-domain methods, Cristalli [16] and Lv [17] have developed a kurtosis-based approach for the detection of speckle noise in laser captured signals. As the kurtosis can measure the degree of deviation from a certain distribution [18], it can be applied to identify abnormal speckle samples.…”

Section: Related Workmentioning

confidence: 99%

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Speckle Noise Detection and Removal for Laser Speech Measurement Systems

2021

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Laser speech measurement is a new sound capture technology based on Laser Doppler Vibrometry (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.

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Section: Related Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

2021

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“…In the initial detection step, the raw signal is segmented using a rectangular window. The region that is larger than a certain threshold based on the Kurtosis ratio is then considered to be contaminated by speckle noise (see [24], [25] for a detailed discussion of this technique). After locating the area affected by speckle noise, the corrupted samples can be replaced by a collection, N, of samples previously established to be clean.…”

Section: Related Workmentioning

confidence: 99%

Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

Wang

Zhang

Wang

2021

IEICE Trans. Inf. & Syst.

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Laser speech detection uses a non-contact Laser Doppler Vibrometry (LDV)-based acoustic sensor to obtain speech signals by precisely measuring voice-generated surface vibrations. Over long distances, however, the detected signal is very weak and full of speckle noise. To enhance the quality and intelligibility of the detected signal, we designed a two-sided Linear Prediction Coding (LPC)-based locator and interpolator to detect and replace speckle noise. We first studied the characteristics of speckle noise in detected signals and developed a binary-state statistical model for speckle noise generation. A two-sided LPC-based locator was then designed to locate the polluted samples, composed of an inverse decorrelator, nonlinear filter and threshold estimator. This greatly improves the detectability of speckle noise and avoids false/missed detection by improving the noise-to-signal-ratio (NSR). Finally, samples from both sides of the speckle noise were used to estimate the parameters of the interpolator and to code samples for replacing the polluted samples. Real-world speckle noise removal experiments and simulation-based comparative experiments were conducted and the results show that the proposed method is better able to locate speckle noise in laser detected speech and highly effective at replacing it.

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“…In this case, no light reaches the photodetector at a certain point in time and consequently no information about the vibration can be obtained. The impact of this effect is the subject of several publications and has already been widely researched [ 9 , 10 , 11 , 12 , 13 ]. Even though the effect can be utilized for some specific measurement methods [ 14 , 15 ], in most LDV applications, it reduces the signal quality and limits the minimally detectable amplitude of a vibration [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination

Schewe

Rembe

2021

Sensors

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The intensity of the reflected measuring beam is greatly reduced for laser-Doppler vibrometer (LDV) measurements on rough surfaces since a considerable part of the light is scattered and cannot reach the photodetector (laser speckle effect). The low intensity of the reflected laser beam leads to a so-called signal dropout, which manifests as noise peaks in the demodulated velocity signal. In such cases, no light reaches the detector at a specific time and, therefore, no signal can be detected. Consequently, the overall quality of the signal decreases significantly. In the literature, first attempts and a practical implementation to reduce this effect by signal diversity can be found. In this article, a practical implementation with four measuring heads of a Multipoint Vibrometer (MPV) and an evaluation and optimization of an algorithm from the literature is presented. The limitations of the algorithm, which combines velocity signals, are shown by evaluating our measurements. We present a modified algorithm, which generates a combined detector signal from the raw signals of the individual channels, reducing the mean noise level in our measurement by more than 10 dB. By comparing the results of our new algorithm with the algorithms of the state-of-the-art, we can show an improvement of the noise reduction with our approach.

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The effect of speckles noise on the Laser Doppler Vibrometry for remote speech detection

Cited by 26 publications

References 19 publications

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

Speckle Noise Detection and Removal for Laser Speech Measurement Systems

Two-Sided LPC-Based Speckle Noise Removal for Laser Speech Detection Systems

Signal Diversity for Laser-Doppler Vibrometers with Raw-Signal Combination

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