Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

Zu, Xingshui; Guo, Feng; Huang, Jingchang; Zhao, Q.; Liu, Huawei; Li, Baoqing; Yuan, Xia

doi:10.3390/s17030514

Cited by 19 publications

(12 citation statements)

References 16 publications

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“…An array of microphones has been traditionally used for localizing objects. For example, some works [28]- [30] have leveraged the acoustic array as a wild area surveillance sensor for tracking target movement or detecting target intrusion. However, these works require a specialized hardware, such as an array of four or more microphones, for localizing objects.…”

Section: Related Work a Microphone Array-based Sound Source Locamentioning

confidence: 99%

TapSnoop: Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices

2020

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We propose a novel tapstroke inference attack method, called TapSnoop, that precisely recovers what user types on touchscreen devices. Inferring tapstrokes is challenging owing to 1) low tapstroke intensity and 2) dynamically-changing noise. We address these challenges by revealing the unique characteristics of tapstrokes from audio recordings exploited by TapSnoop as a side channel of tapstrokes. In particular, we develop tapstroke detection and localization algorithms that collectively leverage audio features obtained from multiple microphones, which are designed to reflect the core properties of tapstrokes. Furthermore, we improve its robustness against environmental changes, by developing environment-adaptive classification and noise subtraction algorithms. Extensive experiments with ten real-world users on both number and QWERTY keyboards show that TapSnoop can achieve an inference accuracy of 85.4% and 75.6% (96.2% and 90.8% in best case scenarios) in stable environments, respectively. TapSnoop can also achieve a reasonable accuracy even with varying noise. For example, it shows an inference accuracy of 84.8% and 72.7% in a numeric keyboard when the noise level is varied from 37.9 to 51.2 dBA and 46.7 to 60.0 dBA, respectively. INDEX TERMS Acoustic signal processing, acoustic sensors, mobile computing, privacy, side-channel attack, tapstroke inference.

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Section: Related Work a Microphone Array-based Sound Source Locamentioning

confidence: 99%

TapSnoop: Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices

2020

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“…We evaluate the proposed method for moving vehicles classification by comparing with the baseline method, and perform experiments in the framework with the popular classifier Gaussian mixture model (GMM) [17], [29], [30]. In our experiment, we adopt the target detection algorithm presented in [31]. For the two-stage detector we adopted, the first-stage detector reports a suspected target only after that a target could be detected in three sequential frames and it is considered a target invasion when the suspected target could be confirmed in the following frame by the second-stage detector.…”

Section: B Experimentsmentioning

confidence: 99%

An Acoustic-Based Feature Extraction Method for the Classification of Moving Vehicles in the Wild

Zhao

Guo

et al. 2019

IEEE Access

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The acoustic classification technology of moving vehicles is a significant application in wireless sensor networks (WSNs). However, the wild environment makes it more intractable for the acoustic target classification owing to the complicated interference noise. The widely used mel-frequency cepstral coefficients (MFCCs) are somewhile contaminated by the acoustic noise, resulting in degrading the classification performance in real environments. To increase the classification accuracy of the moving vehicles, this paper presents an acoustic feature extraction method, which integrates the nonlinear function with the MFCC method, termed as NMFCC. Through nonlinear transformation, the proposed method could amplify the acoustic features in a nonlinear way, and obtain more robust acoustic features, thereby achieving a superior classification performance. Comparing the NMFCC with the baseline feature extraction method, experimental results not only demonstrate the viability of the proposed algorithm but also show a satisfactory performance of the moving vehicle classification for diverse practical scenarios in the wild. INDEX TERMS Acoustic feature extraction, moving target classification, nonlinear function, mel-frequency cepstral coefficients, microphone array.

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“…In previous studies, our research team has designed a small-aperture microphone array system [23] for recognizing moving vehicle targets and has carried out related algorithm research in acoustic signal enhancement [24], target detection [25], [26], target classification [27], [28] and DOA estimation [29]- [31]. Based on our previous research, we aimed to design a microphone array node with ultra-low power by equipping the proposed robust practical algorithms.…”

Section: Introductionmentioning

confidence: 99%

Single-Mode Wild Area Surveillance Sensor With Ultra-Low Power Design Based on Microphone Array

et al. 2019

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Acoustic sensors are used as an important sensor component in wireless sensor networks (WSNs) for wild environmental monitoring, because of its small size, light weight, and low power consumption. To further utilize the superiorities of acoustic sensor, this paper aims to design a single-mode wild area surveillance sensor based on acoustic sensors, which have the advantages of practicality, low hardware power consumption, and low software complexity. The proposed algorithm can effectively suppress wind noise and improve the performance of target detection, classification and direction of arrival (DOA) estimation. The power consumption test results show that the designed micro-sensor node has low power consumption of about 13.8 mW in on-duty mode and long-term continuous monitoring capability of about 33 days. The field experiment results reveal that the node has favorable performance in detection (detection rate as high as 96% with false alarm rate under 5%), tracking (error is less than 7.6 degree), and target classification (accuracy is greater than 92.6%).

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Design of an Acoustic Target Intrusion Detection System Based on Small-Aperture Microphone Array

Cited by 19 publications

References 16 publications

TapSnoop: Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices

TapSnoop: Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices

An Acoustic-Based Feature Extraction Method for the Classification of Moving Vehicles in the Wild

Single-Mode Wild Area Surveillance Sensor With Ultra-Low Power Design Based on Microphone Array

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