Vehicle detection using acoustic signatures

Uttarakumari, M; Koushik, Anirudh S; Raghavendra, Anirudh S; Adiga, Akshay; Patel, Harshita

doi:10.1109/ccaa.2017.8229975

Cited by 4 publications

(1 citation statement)

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“…Vehicles, like many other objects with rotary or oscillatory components, can be identified from the frequency content of their acoustic signature. Hence, wavelet analysis, which classifies objects based on temporal frequency content, has been used in AVDC-like applications [8]. However, wavelet analysis is energy-intensive due to the involved digital computations.…”

Section: Wireless Sensor Network and Acoustic Techniquesmentioning

confidence: 99%

An Acoustic Vehicle Detector and Classifier Using a Reconfigurable Analog/Mixed-Signal Platform

Bhattacharyya

Andryzcik

Graham

2020

JLPEA

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The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 μ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes.

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Section: Wireless Sensor Network and Acoustic Techniquesmentioning

confidence: 99%