Classification of ground vehicles using acoustic signal processing and neural network classifier

Kandpal, Manisha; Kakar, Varun Kumar; Verma, Gaurav

doi:10.1109/icspcom.2013.6719846

Cited by 10 publications

(3 citation statements)

References 3 publications

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“…It uses the engine's sound to categorize the vehicles. [121] Signal processing and analysis through neural network. [122] Parking management system solved via vehicle counting entry in park houses.…”

Section: Acoustic Sensormentioning

confidence: 99%

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

Micko

Papcun

Zolotová

2023

Sensors

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An intelligent transportation system is one of the fundamental goals of the smart city concept. The Internet of Things (IoT) concept is a basic instrument to digitalize and automatize the process in the intelligent transportation system. Digitalization via the IoT concept enables the automatic collection of data usable for management in the transportation system. The IoT concept includes a system of sensors, actuators, control units and computational distribution among the edge, fog and cloud layers. The study proposes a taxonomy of sensors used for monitoring tasks based on motion detection and object tracking in intelligent transportation system tasks. The sensor’s taxonomy helps to categorize the sensors based on working principles, installation or maintenance methods and other categories. The sensor’s categorization enables us to compare the effectiveness of each sensor’s system. Monitoring tasks are analyzed, categorized, and solved in intelligent transportation systems based on a literature review and focusing on motion detection and object tracking methods. A literature survey of sensor systems used for monitoring tasks in the intelligent transportation system was performed according to sensor and monitoring task categorization. In this review, we analyzed the achieved results to measure, sense, or classify events in intelligent transportation system monitoring tasks. The review conclusions were used to propose an architecture of the universal sensor system for common monitoring tasks based on motion detection and object tracking methods in intelligent transportation tasks. The proposed architecture was built and tested for the first experimental results in the case study scenario. Finally, we propose methods that could significantly improve the results in the following research.

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Section: Acoustic Sensormentioning

confidence: 99%

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

Micko

Papcun

Zolotová

2023

Sensors

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“…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.…”

Section: Machine/vehicle Classification From Other Sensing Systemsmentioning

confidence: 99%

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

Tejedor

Macías-Guarasa

Martins³

et al. 2017

Applied Sciences

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Abstract:There is an increasing interest in researchers and companies on the combination of Distributed Acoustic Sensing (DAS) and a Pattern Recognition System (PRS) to detect and classify potentially dangerous events that occur in areas above fiber optic cables deployed along active pipelines, aiming to construct pipeline surveillance systems. This paper presents a review of the literature in what respect to machine learning techniques applied to pipeline surveillance systems based on DAS+PRS (although its scope can also be extended to any other environment in which DAS+PRS strategies are to be used). To do so, we describe the fundamentals of the machine learning approaches when applied to DAS systems, and also do a detailed literature review of the main contributions on this topic. Additionally, this paper addresses the most common issues related to real field deployment and evaluation of DAS+PRS for pipeline threat monitoring, and intends to provide useful insights and recommendations in what respect to the design of such systems. The literature review concludes that a real field deployment of a PRS based on DAS technology is still a challenging area of research, far from being fully solved.

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“…Furthermore, various studies have used other features as indicators instead of SQ factors. A classification model was created using a short-time Fourier transform (STFT) [15][16][17]. Some studies have classified vehicles using Mel-frequency cepstral coefficient (MFCC) [18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Statistical Classification of Vehicle Interior Sound Through Upsampling-Based Augmentation and Correction Using 1D CNN and LSTM

Kim

Lee

2022

IEEE Access

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To quantitatively classify the results provided by engineers, we built a sound quality (SQ) classification model using neural networks. The data used in this study were recorded wav files obtained from various vehicle specifications while driving under wide open throttle conditions. However, the data lengths were not constant. The upsampling and interpolation scheme (USIS) was used to achieve constant data lengths. After the USIS was applied to the data, dynamic time warping was used to verify that there was no change in the data characteristics. The verified dataset was transformed into Mel-spectrogram to confirm the characteristics, and dimensionality reduction was applied by using a high-pass filter. Clarifying the differences between clusters improves the model performance. The classification models of 1D convolutional neural network and long short-term memory exhibited training accuracies of about 94.9% (64 or 65 out of 68 classified) and test accuracies of about 87.5% (7 out of 8 classified). For additional undefined label classification, the quantitative evaluation and statistical classification of undefined sound quality labels are successfully identified in the present study. Both neural networks produced effective results that can be used by sound design engineers to quantitatively examine the SQ of vehicle interior noise.INDEX TERMS upsampling and interpolation, dynamic time warping, mel-spectrogram, sound quality, classification

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Classification of ground vehicles using acoustic signal processing and neural network classifier

Cited by 10 publications

References 3 publications

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

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

Statistical Classification of Vehicle Interior Sound Through Upsampling-Based Augmentation and Correction Using 1D CNN and LSTM

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