Acoustic signal based traffic density state estimation using adaptive Neuro-Fuzzy classifier

Borkar, Prashant; Malik, Latesh

doi:10.1109/fuzz-ieee.2013.6622444

Cited by 6 publications

(8 citation statements)

References 22 publications

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“…In our previous work, we have estimated vehicular traffic density states using acoustic signal [37]. In the first part it provides brief overview of acoustic work in the field of vehicular speed estimation, vehicular traffic density estimation, and vehicular classification and in second part we have applied Adaptive Neuro-Fuzzy Classifier to classify the traffic density states as low, medium, and heavy.…”

Section: Audio Modality For Traffic Density State Estimationmentioning

confidence: 99%

Modality of Adaptive Neuro-Fuzzy Classifier for Acoustic Signal-Based Traffic Density State Estimation Employing Linguistic Hedges for Feature Selection

Borkar¹,

Sarode²,

Malik³

2015

Int. J. Fuzzy Syst.

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Modality of adaptive neuro-fuzzy classifier (ANFC) for vehicular traffic density estimation using linguistic hedges (LH) and feature selection (FS) approach is proposed in this work. Cumulative vehicular acoustic signal is collected from roadside installed Omni-directional microphone followed by acoustic feature extraction using Mel frequency cepstral coefficients for varying combination of frame size and shift size. ANFC is modeled to classify traffic density states as low, medium, and heavy. Classification performance is further improved through modeling of ANFC with LH. Feature selection criteria are considered for varying combinations of frame size and shift, where linguistic hedges are employed for FS followed by ANFC to model traffic density states. We are getting better classification performance as compared to state of art literature for lower combinations of frame and shift size and even for consideration of single frame feature vector. Consideration of multiple contiguous frames will definitely increase the accuracy but with cost of computational time.

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Section: Audio Modality For Traffic Density State Estimationmentioning

confidence: 99%

Modality of Adaptive Neuro-Fuzzy Classifier for Acoustic Signal-Based Traffic Density State Estimation Employing Linguistic Hedges for Feature Selection

Borkar¹,

Sarode²,

Malik³

2015

Int. J. Fuzzy Syst.

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“…The neuro-fuzzy modelling method has been compared with the extraction of mathematical information which describes the suitable character of this technique. By applying the ANFC method, a basic fuzzy model would be generated with the rules extracted from the input and output data [19]. Followed by, the NN is employed for tuning the basic method rules to produce excellent ANFC framework.…”

Section: Anfc Based Classification Modelmentioning

confidence: 99%

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

Siddiqa¹,

Naqvi²,

Ahsan³

et al. 2022

Computers, Materials &Amp; Continua

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Recent advancements in computer technologies for data processing, collection, and storage have offered several chances to improve the abilities in production, services, communication, and researches. Data mining (DM) is an interdisciplinary field commonly used to extract useful patterns from the data. At the same time, educational data mining (EDM) is a kind of DM concept, which finds use in educational sector. Recently, artificial intelligence (AI) techniques can be used for mining a large amount of data. At the same time, in DM, the feature selection process becomes necessary to generate subset of features and can be solved by the use of metaheuristic optimization algorithms. With this motivation, this paper presents an improved evolutionary algorithm based feature subsets election with neuro-fuzzy classification (IEAFSS-NFC) for data mining in the education sector. The presented IEAFSS-NFC model involves data pre-processing, feature selection, and classification. Besides, the Chaotic Whale Optimization Algorithm (CWOA) is used for the selection of the highly related feature subsets to accomplish improved classification results. Then, Neuro-Fuzzy Classification (NFC) technique is employed for the classification of education data. The IEAFSS-NFC model is tested against a benchmark Student Performance DataSet from the UCI repository. The simulation outcome has shown that the IEAFSS-NFC model is superior to other methods.

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“…The audible sound on a road is a combination of several sound sources such as the engines, the exhausts, the wheels and air turbulence, which occur when vehicles pass by [1]. As a reasonable way to break down this complex audio analysis task, researches approached traffic monitoring from different perspectives.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, most ATM methods analyze either stereo audio signals [10], which are recorded with microphone arrays [7]- [9]. Microphones are commonly integrated into sensor units, which are either placed at the roadside [3] [4] [8] [2] or mounted on light poles at a height between 0.5 and 3 meters [1] [7] [6].…”

Section: Related Workmentioning

confidence: 99%

IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

Abeßer¹,

Gourishetti²,

Kátai³

et al. 2021

Preprint

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In many urban areas, traffic load and noise pollution are constantly increasing. Automated systems for traffic monitoring are promising countermeasures, which allow to systematically quantify and predict local traffic flow in order to to support municipal traffic planning decisions. In this paper, we present a novel open benchmark dataset, containing 2.5 hours of stereo audio recordings of 4718 vehicle passing events captured with both high-quality sE8 and medium-quality MEMS microphones. This dataset is well suited to evaluate the use-case of deploying audio classification algorithms to embedded sensor devices with restricted microphone quality and hardware processing power. In addition, this paper provides a detailed review of recent acoustic traffic monitoring (ATM) algorithms as well as the results of two benchmark experiments on vehicle type classification and direction of movement estimation using four state-of-the-art convolutional neural network architectures.

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Acoustic signal based traffic density state estimation using adaptive Neuro-Fuzzy classifier

Cited by 6 publications

References 22 publications

Modality of Adaptive Neuro-Fuzzy Classifier for Acoustic Signal-Based Traffic Density State Estimation Employing Linguistic Hedges for Feature Selection

Modality of Adaptive Neuro-Fuzzy Classifier for Acoustic Signal-Based Traffic Density State Estimation Employing Linguistic Hedges for Feature Selection

An Improved Evolutionary Algorithm for Data Mining and Knowledge Discovery

IDMT-Traffic: An Open Benchmark Dataset for Acoustic Traffic Monitoring Research

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