Vehicle acoustic classification in netted sensor systems using Gaussian mixture models

Automated Acoustic Identification of Vehicles

“…Acoustic signals of vehicles are mostly generated by engine, propulsion, exhaust system, vibration of the body and also friction noise between tyres and the ground surface. [23][24][25][26][27][28][29][30] It was suggested that the dominant components of acoustic signals of a vehicle moving faster than 30 miles per hour (approximately 48 km per hour) are due to tyre friction noise. 31,32 The major elements of moving vehicle signals are observed in the relatively lower frequency part of human perception range, e.g.…”

Deep Learning Based Threat Classification for Fiber Optic Distributed Acoustic Sensing Using SNR Dependent Data Generation

“…In literature, there are some traditional machine learning methods exists such as Support Vector Machine (SVM) [13], k-Nearest Neighbor (k-NN) [14], Principal Component Analysis (PCA) and Artificial Neural Network (ANN) [15]. In addition, other machine learning methods includes Gaussian Mixture Model (GMM) [16] and Neural Network (NN) [17] methods come into prominence in ODTR classification studies. On the other hand, recently developed machine learning method Deep Learning (DL) [5] has also been used to achieve good performance in this context.…”

Machine Learning Methods for Pipeline Surveillance Systems Based on Distributed Acoustic Sensing: A Review

“…We include here some references in this area to quantify the state of the art results that can be expected in this related research scenario, in which various strategies for the design of the PRS (mainly feature extraction and classification) have been used [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. Table 1 shows the main features of those signal classification systems, in terms of the sensing method, feature extraction, classification algorithm employed, classification task, and classification accuracy.…”