# Speckle Noise Detection and Removal for Laser Speech Measurement Systems
## 2 Related Works
Focusing directly upon impulsive noise, [PERSON] has proposed an Autoregressive (AR)--based impulsive noise detection [19] and interpolation [20] method that can be used in the detection phase. He transformed an original noisy signal into an excitation signal, while keeping the impulsive noise either unchanged or increased, by drawing on an AR model in order to increase the detection accuracy. After transforming the direct threshold by the estimated value of the excitation standard-deviation to locate the impulsive noise, the AR model can be used to generate samples to replace the noise samples and obtain an enhanced signal. This approach is very effective and can manage the targeted removal of impulsive noise. However, the impulsive noise detection accuracy is undermined by having to establish a direct threshold, especially if the transformed signal still contains a large amount of background noise. Recently, some data driven methods have been proposed to suppress noise [21; 22]. However, they all focus on background noise. For instance, [PERSON] [23] has proposed a neural network-based architecture for VAD that works on a typical short audio frame basis. While the state-of-the-art neural network based VADs can achieve very good results, they often exceed computational budgets and cannot meet real-time operating requirements. [PERSON] [24] has presented a novel method to computationally determine when video data contains a person speaking through the recognition of full-lip facial closures within a given interval. However, the timing of video and sound detection is often not consistent and the problem of noise when processing the voice remains. Therefore, it is not feasible to use mouth movements to detect noise in laser-captured speech. In the interpolation phase, several methods have been developed for the interpolation of missing samples in music or speech signals, which closely resembles the concern with noise removal based on accurately positioning the noise in speech detection. While some interpolation techniques, such as median filtering, are completely blind (no hypothesis regarding the signal is made) [25], they are often too crude to reconstruct gaps that are larger than a few samples. The noise frame can be set to zero. However, this will destroy the periodicity of the speech signal, leading to frequency truncation, causing sudden changes in the frequency between the speech frame and the enhanced frame, and resulting in an audible \"popping\" sound. So, when a noise frame is located, more appropriate methods need to be selected to obtain the enhanced signal. In summary, although several studies have been devoted to detecting and removing the noise in speech, most approaches focus on eliminating background noise. In the case of single judgment-based methods, the recognition rate for speckle noise is often low. Some novel data-driven methods have produced promising results. However, they are computationally expensive and time consuming. Therefore, they are not suitable for applications that depend on real time processing. Compared with these relevant methods, we fully analyze the characteristics of speckle noise in laser measured speech. On this basis, a novel automatic speckle noise detection and removal method is proposed. This method first foregrounds noise using decorrelation based on a linear prediction (LP) model that improves the NSR of the measured signal. This allows detection of the position of speckle 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 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.