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

Zhao, Q.; Guo, Feng; Zu, Xingshui; Li, Baoqing

doi:10.1109/access.2019.2920847

Cited by 6 publications

(2 citation statements)

References 33 publications

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“…One of the reasons for the popularity of the MFCC is its antinoise capability [25,27,31]. However, this statement contradicts the statement of Ji et al [22] based on the result of Zhao et al [43]. Furthermore, the method proposed by Choi et al [26] using the dominant neighborhood structure (DNS) algorithm as an extraction method has shown superior performance in a noisy environment.…”

Section: Feature Extraction and Fusion Techniquesmentioning

confidence: 96%

Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming

Lagua

Mun

Ampode

et al. 2023

Animals

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Porcine respiratory disease complex is an economically important disease in the swine industry. Early detection of the disease is crucial for immediate response to the disease at the farm level to prevent and minimize the potential damage that it may cause. In this paper, recent studies on the application of artificial intelligence (AI) in the early detection and monitoring of respiratory disease in swine have been reviewed. Most of the studies used coughing sounds as a feature of respiratory disease. The performance of different models and the methodologies used for cough recognition using AI were reviewed and compared. An AI technology available in the market was also reviewed. The device uses audio technology that can monitor and evaluate the herd’s respiratory health status through cough-sound recognition and quantification. The device also has temperature and humidity sensors to monitor environmental conditions. It has an alarm system based on variations in coughing patterns and abrupt temperature changes. However, some limitations of the existing technology were identified. Substantial effort must be exerted to surmount the limitations to have a smarter AI technology for monitoring respiratory health status in swine.

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Section: Feature Extraction and Fusion Techniquesmentioning

confidence: 96%

Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming

Lagua

Mun

Ampode

et al. 2023

Animals

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“…For vast border areas, wilderness areas, scattered warehouses, etc., detecting intrusions, although rarely happening, is very important for security reasons. Targets such as human beings, vehicles, and wildlife, are of constant concern for both civilian and military use [ 39 , 66 , 73 , 74 , 75 , 76 ]. Traditional high-power monitoring methods, such as live cameras, require a power grid for power supply which is impractical for many applications.…”

Section: Applicationsmentioning

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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Detecting earthquakes: a novel deep learning-based approach for effective disaster response

et al. 2021

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

Cited by 6 publications

References 33 publications

Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming

Artificial Intelligence for Automatic Monitoring of Respiratory Health Conditions in Smart Swine Farming

Acoustic Wake-Up Technology for Microsystems: A Review

Detecting earthquakes: a novel deep learning-based approach for effective disaster response

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