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

Liu, Huawei; Jun, Shi; Huang, Jingchang; Zhou, Qianwei; Wei, Shuang; Li, Baoqing

doi:10.1109/access.2019.2921673

Cited by 9 publications

(3 citation statements)

References 32 publications

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“…Additionally, these networks could potentially identify sounds by improving network coverage [8]. To illustrate, the author developed a deforestation detector in [9] as part of the fight against the destruction of forest areas.…”

Section: Physics and Astronomymentioning

confidence: 99%

Improvement of chainsaw sounds identification in the forest environment using maximum ratio combining and classification algorithme

Gnamele,

Youan,

Famien

2024

Eureka: PE

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To better combat the devastation of the protected forests in Côte d’Ivoire, a study was conducted to create a technique for detecting the acoustic signals produced by chainsaws deployed to fell trees in these areas. To improve the recognition rate of chainsaw sounds in a forest environment and increase the detection range of the recognition system, we are implementing the maximum ratio combining (MRC) technique on a microphone array. Therefore, the employment of an identification system is compared using one (01) microphone against the outcomes obtained by adopting system with three (03), six (06), and twelve (12) microphones. The use of MRC is then contrasted with an alternative recombining approach, referred to as simple summation (SS). The SS is characterized by the mere addition of signals acquired by the network in the frequency domain. The MRC was employed on various microphone arrangements, accounting for varying degrees of attenuation experienced by chainsaw sounds. The K-Nearest Neighbors, in combination with Mel Frequency Cepstral Coefficients (MFCC), was employed to detect chainsaw sounds within the 16 kHz central frequency octave band. MRC applied to microphone arrays provided superior outcomes than simple summation. The enhancement in terms of classification rate ranged from [18; 51], favouring MRC. Moreover, it extended the chainsaw detection range from 520 m (using one microphone) to 1210 m (using a 12-microphone array). Taking into account the criteria for selecting an optimum microphone array, including classification rate, number of microphone nodes, information processing time and detection range, the six-microphone array was chosen as the best configuration. This configuration boasts a theoretical detection range of 1040 meters

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Section: Physics and Astronomymentioning

confidence: 99%

Improvement of chainsaw sounds identification in the forest environment using maximum ratio combining and classification algorithme

Gnamele,

Youan,

Famien

2024

Eureka: PE

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“…An acoustic wake-up microsystem in this architecture is reported, which achieves target detecting, classifying, and tracking in the real wild area, as shown in Figure 17 a [ 61 ]. The microsystem consumes 13.8 mW in sleep mode and has a long-term continuous monitoring capability of about 33 days.…”

Section: System Wake-up Architecturementioning

confidence: 99%

Acoustic Wake-Up Technology for Microsystems: A Review

Yang

Zhao

2023

Micromachines

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Microsystems with capabilities of acoustic signal perception and recognition are widely used in unattended monitoring applications. In order to realize long-term and large-scale monitoring, microsystems with ultra-low power consumption are always required. Acoustic wake-up is one of the solutions to effectively reduce the power consumption of microsystems, especially for monitoring sparse events. This paper presents a review of acoustic wake-up technologies for microsystems. Acoustic sensing, acoustic recognition, and system working mode switching are the basis for constructing acoustic wake-up microsystems. First, state-of-the-art MEMS acoustic transducers suitable for acoustic wake-up microsystems are investigated, including MEMS microphones, MEMS hydrophones, and MEMS acoustic switches. Acoustic transducers with low power consumption, high sensitivity, low noise, and small size are attributes needed by the acoustic wake-up microsystem. Next, acoustic features and acoustic classification algorithms for target and event recognition are studied and summarized. More acoustic features and more computation are generally required to achieve better recognition performance while consuming more power. After that, four different system wake-up architectures are summarized. Acoustic wake-up microsystems with absolutely zero power consumption in sleep mode can be realized in the architecture of zero-power recognition and zero-power sleep. Applications of acoustic wake-up microsystems are then elaborated, which are closely related to scientific research and our daily life. Finally, challenges and future research directions of acoustic wake-up microsystems are elaborated. With breakthroughs in software and hardware technologies, acoustic wake-up microsystems can be deployed for ultra-long-term and ultra-large-scale use in various fields, and play important roles in the Internet of Things.

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

Cited by 9 publications

References 32 publications

Improvement of chainsaw sounds identification in the forest environment using maximum ratio combining and classification algorithme

Improvement of chainsaw sounds identification in the forest environment using maximum ratio combining and classification algorithme

Acoustic Wake-Up Technology for Microsystems: A Review

TapSnoop: Leveraging Tap Sounds to Infer Tapstrokes on Touchscreen Devices

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