Automated Accident Detection in Intersections via Digital Audio Signal Processing

2003 ◽  
Vol 1840 (1) ◽  
pp. 186-192 ◽  
Author(s):  
Lori Mann Bruce ◽  
Navaneethakrishnan Balraj ◽  
Yunlong Zhang ◽  
Qingyong Yu
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Maximum Likelihood ◽  
Nearest Neighbor ◽  
Signal To Noise Ratio ◽  
Audio Signal ◽  
Feature Reduction ◽  
Discrete Wavelet ◽  
Feature Optimization ◽  
Accident Detection

A system for automated traffic accident detection in intersections was designed. The input to the system is a 3-s segment of audio signal. The system can be operated in two modes: the two-class and multiclass modes. The output of the two-class mode is a label of “crash” or “noncrash.” In the multiclass mode of operation, the system identifies crashes as well as several types of noncrash incidents, including normal traffic and construction sounds. The system is composed of three main signal processing stages: feature extraction, feature reduction, and classification. Five methods of feature extraction were investigated and compared; these are based on the discrete wavelet transform, fast Fourier transform, discrete cosine transform, real cepstral transform, and mel frequency cepstral transform. Statistical methods are used for feature optimization and classification. Three types of classifiers are investigated and compared; these are the nearest-mean, maximum-likelihood, and nearest-neighbor methods. The results of the study show that the optimum design uses wavelet-based features in combination with the maximum-likelihood classifier. The system is computationally inexpensive relative to the other methods investigated, and the system consistently results in accident detection accuracies of 95% to 100% when the audio signal has a signal-to-noise-ratio of at least 0 decibels.

scholarly journals WAVELET-CONVERSION IN ELECTROCARDIO SIGNAL PROCESSING

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
V.F. Telezhkin ◽  
◽  
B.B. Saidov ◽  
P.А. Ugarov ◽  
A.N. Ragozin ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Digital Signal ◽  
Automated Analysis ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Signal To Noise ◽  
Time Frequency ◽  
Fourth Level

In the present work, processing of an electro cardio signal using a wavelet transform is consi-dered. In electrocardiography, various digital signal-processing techniques are used to detect, extract, and analyze the various components of an electrocardiogram. Among them, the wavelet transform technique gives promising results in the analysis of the time-frequency characteristics of the electrocardiogram components. The urgency of solving the problem of improving the quality of life of people with the help of early diagnosis and timely treatment of various cardiac diseases is obvious. The process of automated analysis of a huge database of electrocardiographic data is especially important. Wavelet analysis can be successfully used to smooth and remove noise in the ECG signal. Electrocardiogram signal, cleaned from noise components, looks clearer, while its volume is from 10 to 5% of the original signal, which largely solves the problem of storing cardiac records. Aim. Development of an algorithm for threshold processing of wavelet coefficients and filtering of an electrocardiography signal. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the MATLAB environment that implements continuous and discrete wavelet transform. Results. The work shows the result of filtering the ECG signal with the addition of noise with a signal-to-noise ratio of 35 and 45 dB using the decomposition levels N = 2, N = 3, N = 4. Conclusion. Based on the analysis of the data obtained, it can be concluded that the second level of decomposition is the most optimal for filtering the ECG signal. With an increase in the level of decomposition, the output ratio decreases, at the level N = 4 the output signal-to-noise almost does not exceed the input one, therefore, the filtering becomes ineffective. The correlation coefficient to the fourth level is significantly reduced, which means a significant increase in the distortion introduced by the filtering algorithm.

scholarly journals Matched Filter Based Detection over Time Varying Fading Channels with Reduced Complexity

2019 ◽  
pp. 1-6
Author(s):  
Mohd Israil
Keyword(s):  
Signal Processing ◽  
Maximum Likelihood ◽  
Fading Channels ◽  
High Speed ◽  
Matched Filter ◽  
Signal To Noise Ratio ◽  
Digital Signal ◽  
Time Varying ◽  
High Speed Data ◽  
Over Time

Challenges in high speed data transmission technology over time varying fading channels is addressed in this paper. More precisely, the signal processing at the receiver side has to be analyzed for such systems, as it is well known that the mobile radio channels are characterized by frequency selective fast fading is typically introduced error in the received signal. Thus, the performance of the receiver severely degraded because of such factors. Specifically, this paper deals with the detection using a matched filter followed by low weight near maximum likelihood detector (NMLD) for the application of digital signal processing in outdoor vehicular radio environments. Nearly Maximum Likelihood Detection depends on the length of the stored vectors as well as depends on the numbers of the stored vector. In [1] complexity is reduced by reducing the stored vectors, in this paper same NMLD used but the complexity of the matched filter is reduced by some variance. Finally, the bit error rate (BER) is measured with signal to noise ratio.

scholarly journals A Novel Intelligent System for Brain Tumor Diagnosis Based on a Composite Neutrosophic-Slantlet Transform Domain for Statistical Texture Feature Extraction

2020 ◽  
Vol 2020 ◽  
pp. 1-21
Author(s):  
Shakhawan H. Wady ◽  
Raghad Z. Yousif ◽  
Harith R. Hasan
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Brain Tumor ◽  
Nearest Neighbor ◽  
Intelligent System ◽  
Brain Mri ◽  
Area Under The Curve ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet

Discrete wavelet transform (DWT) is often implemented by an iterative filter bank; hence, a lake of optimization of a discrete time basis is observed with respect to time localization for a constant number of zero moments. This paper discusses and presents an improved form of DWT for feature extraction, called Slantlet transform (SLT) along with neutrosophy, a generalization of fuzzy logic, which is a relatively new logic. Thus, a novel composite NS-SLT model has been suggested as a source to derive statistical texture features that used to identify the malignancy of brain tumor. The MR images in the neutrosophic domain are defined using three membership sets, true (T), false (F), and indeterminate (I); then, SLT was applied to each membership set. Three statistical measurement-based methods are used to extract texture features from images of brain MRI. One-way ANOVA has been applied as a method of reducing the number of extracted features for the classifiers; then, the extracted features are subsequently provided to the four neural network classification techniques, Support Vector Machine Neural Network (SVM-NN), Decision Tree Neural Network (DT-NN), K-Nearest Neighbor Neural Network (KNN-NN), and Naive Bayes Neural Networks (NB-NN), to predict the type of the brain tumor. Meanwhile, the performance of the proposed model is assessed by calculating average accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve. The experimental results demonstrate that the proposed approach is quite accurate and efficient for diagnosing brain tumors when the Gray Level Run Length Matrix (GLRLM) features derived from the composite NS-SLT technique is used.

scholarly journals Feature Extraction of Ship Radiation Signals Based on Wavelet Packet Decomposition and Energy Entropy

2022 ◽  
Vol 2022 ◽  
pp. 1-12
Author(s):  
Yuxing Li ◽  
Feiyue Ning ◽  
Xinru Jiang ◽  
Yingmin Yi
Keyword(s):  
Signal Processing ◽  
Feature Extraction ◽  
Nearest Neighbor ◽  
Wavelet Packet ◽  
Recognition Rate ◽  
Center Frequency ◽  
Permutation Entropy ◽  
Wavelet Packet Decomposition ◽  
K Nearest Neighbor ◽  
Energy Entropy

The analysis of ship radiation signals to identify ships is an important research content of underwater acoustic signal processing. The traditional fast Fourier transform (FFT) is not suitable for analyzing non-stationary, non-Gaussian, and nonlinear signal processing. In order to realize the feature extraction and accurate classification of ship radiation signals with higher accuracy, a feature extraction method of ship radiation signals based on wavelet packet decomposition and energy entropy is proposed in this paper. According to wavelet packet decomposition, the ship radiation signal is decomposed into different frequency bands, and its energy entropy feature is extracted. As for comparisons, the center frequency and permutation entropy are also used as features to be extracted, then the k-nearest neighbor is applied to classify and recognize the extracted results. Based on the comparisons of wavelet packet decomposition, the center frequency, permutation entropy, and the k-nearest neighbor are used for classification and recognition. The experimental results present that, when comparing with center frequency and permutation entropy, the method based on energy entropy has the best availability, with the highest average recognition rate for four types of ship radiation signals, up to 98%.

Classification of Music Mood Using MPEG-7 Audio Features and SVM with Confidence Interval

2018 ◽  
Vol 27 (05) ◽  
pp. 1850016 ◽  
Author(s):  
Riyanarto Sarno ◽  
Johanes Andre Ridoean ◽  
Dwi Sunaryono ◽  
Dedy Rahman Wijaya
Keyword(s):  
Feature Extraction ◽  
Confidence Interval ◽  
Signal Reconstruction ◽  
Audio Signal ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Audio Signals ◽  
Audio Features ◽  
Novel Method ◽  
Mood Classification

Psychologically, music can affect human mood and influence human behavior. In this paper, a novel method for music mood classification is introduced. In the experiment, music mood classification was performed using feature extraction based on MPEG-7 features from the ISO/IEC 15938 standard for describing multimedia content. The result of this feature extraction are 17 low-level descriptors. Here, we used the Audio Power, Audio Harmonicity, and Audio Spectrum Projection features. Moreover, the discrete wavelet transform (DWT) was utilized for audio signal reconstruction. The reconstructed audio signals were classified by the new method, which uses a support vector machine with a confidence interval (SVM-CI). According to the experimental results, the success rate of the proposed method was satisfactory and SVM-CI outperformed the ordinary SVM.

scholarly journals An Active Feature Selection Strategy for DWT in Artificial Taste

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Tao Liu ◽  
Yanbing Chen ◽  
Dongqi Li ◽  
Mengya Wu
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Nearest Neighbor ◽  
Selection Strategy ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Subjective Time ◽  
Feature Size ◽  
Regular Feature ◽  
Time Frequency

A discrete wavelet transform (DWT) extracts meaningful information in a time-frequency domain and is a favorable feature extraction approach from pulse-like responses in large pulse voltammetry (LAPV) electronic tongues (e-tongue). A regular DWT generates lots of coefficients to describe signal details and approximations at different scales. Thus, coefficient selection is necessary to reduce the feature size. However, the common DWT-based feature selection follows a passive mode: manipulation through human experience or exhaustive trials. It is subjective, time consuming, and barely works in nonlaboratory conditions. In this paper, we present an active feature selection strategy consisting of a dispersion ratio computation and optimal searching search. To evaluate the performance of the proposed method, we prepared several beverage samples and performed experiments with a LAPV e-tongue. Meanwhile, the features of raw response, peak-inflection point, referenced DWT method, and our proposed method were presented to indicate the effects of the refined features of the proposed method. Furthermore, we utilized several classifiers such as the k-nearest neighbor (k-NN), support vector machine (SVM), and random forest (RF) to evaluate the improvement of recognition by the refined features. Compared with other regular feature extraction methods, the proposed method can automatically explore high-quality features with an acceptable feature size. Moreover, the highest average accuracy was achieved by the proposed method for each classifier. It is an alternative feature extraction approach for a LAPV e-tongue without any manipulation in real applications.

scholarly journals Recognition of Consumer Preference by Analysis and Classification EEG Signals

2021 ◽  
Vol 14 ◽  
Author(s):  
Mashael Aldayel ◽  
Mourad Ykhlef ◽  
Abeer Al-Nafjan
Keyword(s):  
Feature Extraction ◽  
Nearest Neighbor ◽  
Consumer Preference ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Discrete Wavelet ◽  
K Nearest Neighbor ◽  
Detection Systems ◽  
Power Spectral ◽  
Selection Of

Neuromarketing has gained attention to bridge the gap between conventional marketing studies and electroencephalography (EEG)-based brain-computer interface (BCI) research. It determines what customers actually want through preference prediction. The performance of EEG-based preference detection systems depends on a suitable selection of feature extraction techniques and machine learning algorithms. In this study, We examined preference detection of neuromarketing dataset using different feature combinations of EEG indices and different algorithms for feature extraction and classification. For EEG feature extraction, we employed discrete wavelet transform (DWT) and power spectral density (PSD), which were utilized to measure the EEG-based preference indices that enhance the accuracy of preference detection. Moreover, we compared deep learning with other traditional classifiers, such as k-nearest neighbor (KNN), support vector machine (SVM), and random forest (RF). We also studied the effect of preference indicators on the performance of classification algorithms. Through rigorous offline analysis, we investigated the computational intelligence for preference detection and classification. The performance of the proposed deep neural network (DNN) outperforms KNN and SVM in accuracy, precision, and recall; however, RF achieved results similar to those of the DNN for the same dataset.

Identification of Brain disorders by Sub-band Decomposition of EEG signals and Measurement of Signal to Noise Ratio

2016 ◽  
Vol 4 (3) ◽  
pp. 568
Author(s):  
Hadaate Ullah ◽  
Shahin Mahmud ◽  
Rubana Hoque Chowdhury
Keyword(s):  
Feature Extraction ◽  
Signal To Noise Ratio ◽  
Automated System ◽  
Discrete Wavelet ◽  
Band Pass Filter ◽  
Eeg Signal ◽  
Brain Disorders ◽  
Signal To Noise ◽  
Pass Filter ◽  
Noise Ratio

<p>In the case of medical science, one of the most restless researches is the identification of abnormalities in brain. Electroencephalogram (EEG) is the main tool for determining the electrical activity of brain and it contains rich information associated to the varieties physiological states of brain. The purpose of this task is to identify the EEG signal as order or disorder. It is proposed to enrich an automated system for the identification of brain disorders. An EEG signal of a patient has been taken as a sample. The simulation has been done by MATLAB. The file which consists of the signal has been called in and plotted the signals in MATLAB. The proposed system covers pre-processing, feature extraction, feature selection and classification. By the pre-processing the noises are ejected. In this case the signal has been filtered using band pass filter. The Discrete Wavelet Transform (DWT) has been used to decompose the EEG signal into Sub-band signal. The feature extraction methods have been used to extract the EEG signal into frequency domain and the time domain features. The SNR (Signal to Noise ratio) is obtained in this work is 1.1281dB.<strong></strong></p>

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