Audio signal reconstruction based on adaptively selected seed points from laser speckle images

Chen, Ziyi; Wang, Cheng; Huang, Chaohong; Fu, Hongyan; Luo, Haipeng; Wang, Hanyun

doi:10.1016/j.optcom.2014.05.038

Cited by 24 publications

(10 citation statements)

References 13 publications

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“…The slow computational speed makes it hard to fulfill real time reconstruction of an audio signal using the DIC method. Another technique extracts object vibration using the gray value variation of the speckle image [ 21 , 22 , 23 ]. This approach uses a special algorithm to filter out the appropriate seed points, obtaining the gray value variation of each point and carrying out data fusion to reconstruct the audio signal.…”

Section: Introductionmentioning

confidence: 99%

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

Haruyama

2021

Sensors

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Audio signal acquisition using a laser speckle image is an appealing topic since it provides an accurate and non-contact solution for vibration measurement. However, due to the limitation of camera frame rate and image processing speed, previous research could not achieve real time reconstruction of an audio signal. In this manuscript, we use a one-dimensional laser speckle image to measure the acoustic vibration of sound source and propose a fast and sub-pixel accuracy algorithm to estimate the displacement of captured one-dimensional laser speckle images. Compared with previous research, the proposed method is faster and more accurate in displacement estimation. Owing to this, the frequency bandwidth and the robustness are significantly increased. Experiment results show that the proposed system can achieve 20k samples-per-second sampling rate, and the audio signal can be reconstructed with high quality in real time.

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Section: Introductionmentioning

confidence: 99%

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

Haruyama

2021

Sensors

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“…In our previous researches, the highspeed camera method was adopted and gray value was used to recover the audio signal in a short calculation time. Furthermore, signals from several seed points are fused to increase the SNR of the reconstructed audio signal [5]. To overcome the frame speed limit, a commercially available photodiode combined with a mask is used to measure the speckle flux variations [6].…”

Section: Introductionmentioning

confidence: 99%

Audio Signal Detection and Enhancement Based on Linear CMOS Array and Multi-Channel Data Fusion

Dai

Liu

et al. 2020

IEEE Access

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An audio signal detection system based on laser speckle and multi-channel data fusion is presented. A linear CMOS array is used as the detector, which owns a fast line rate and suitable sensing size. The signals from the pixels are selected and fused to enhance the reconstructed signal. The reconstructed audio signals are evaluated with a segmental SNR (SegSNR) algorithm. The experimental results of three categories of audio sources (single voice audio, conversation and music) show that data fusion can improve the SegSNR scores. Especially, direct phase-error based filtering (pbf) fusion gives a nearly 3.0 dB increase and obtains another 1.0 dB increase with the combination of single channel process. The experimental results show that the fusion algorithms are not sensitive to audio types and the performance of multi-channel data fusion is not weakened with the increase of measuring distance. This feature has potential applications in remote sensing. The intelligibility of the fused audio signals is evaluated with normalized subband envelope correlation (NSEC) algorithm and the evaluation results shows that fusion can also enhance the intelligibility of the recovered signal.INDEX TERMS Audio signal detection and enhancement, linear CMOS array, multi-channel data fusion.

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“…Generally, LDVs are based on the principle of laser interferometry, making LDVs highly sensitive to object surface reflections, environmental factors, and the mutual locations of the projection laser and the detection interferometer modules [ 9 ]. Recently, an emerging technology, image-based sound recovery from high-speed videos, has drawn much attention [ 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 ]. In these systems, a highly developed phase-based algorithm is applied to extract sounds from the high-speed videos that can show subtle motions [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…[ 13 , 14 ] use an efficient singular value decomposition (SVD)-based approach to recover sound information in the high-speed videos. In addition, it has been shown that with an appropriate optical schematic, the sound can be retrieved from the displacements [ 15 ] or the intensity variations [ 16 , 17 ] of the speckle patterns captured with a high-speed camera. Due to the high frame rates, high-speed cameras can record object motions, including sound vibrations, with less influence of circumstances.…”

Section: Introductionmentioning

confidence: 99%

A High-Speed Imaging Method Based on Compressive Sensing for Sound Extraction Using a Low-Speed Camera

Zhu

Yao

Sun

et al. 2018

Sensors

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This paper reports an efficient method for sound extraction from high-speed light spot videos reconstructed from the coded light spot images captured with a low-speed camera based on compressive sensing, but at the expense of consuming time. The proposed method first gets the high-speed video of the light spot that is illuminated on the vibrating target caused by sound. Then the centroid of the light spot is used to recover the sound. Simulations of the proposed method are carried out and experimental results are demonstrated. The results show that high-speed videos with a frame rate of 2000 Hz can be reconstructed with a low-speed (100 Hz) charge-coupled device (CCD) camera, which is randomly modulated by a digital micro-mirror device (DMD) 20 times during each exposure time. This means a speed improvement of 20 times is achieved. The effects of synchronization between CCD image recording and DMD modulation, the optimal sampling patterns of DMD, and sound vibration amplitudes on the performance of the proposed method are evaluated. Using this compressive camera, speech (counting from one to four in Chinese) was recovered well. This has been confirmed by directly listening to the recovered sound, and the intelligibility value (0–1) that evaluated the similarity between them was 0.8185. Although we use this compressive camera for sound detection, we expect it to be useful in applications related to vibration and motion.

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Audio signal reconstruction based on adaptively selected seed points from laser speckle images

Cited by 24 publications

References 13 publications

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

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

Audio Signal Detection and Enhancement Based on Linear CMOS Array and Multi-Channel Data Fusion

A High-Speed Imaging Method Based on Compressive Sensing for Sound Extraction Using a Low-Speed Camera

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