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Article
Publication date: 26 March 2021

Hima Bindu Valiveti, Anil Kumar B., Lakshmi Chaitanya Duggineni, Swetha Namburu and Swaraja Kuraparthi

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance…

132

Abstract

Purpose

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

Design/methodology/approach

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Findings

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Practical implications

Denoising of the audio samples for perfect feature extraction was a tedious chore.

Originality/value

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

Details

International Journal of Pervasive Computing and Communications, vol. 17 no. 3
Type: Research Article
ISSN: 1742-7371

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Article
Publication date: 12 November 2021

D. Vijaya Saradhi, Swetha Katragadda and Hima Bindu Valiveti

A huge variety of devices accumulates as well distributes a large quantity of data either with the help of wired networks or wireless networks to implement a wide variety of…

57

Abstract

Purpose

A huge variety of devices accumulates as well distributes a large quantity of data either with the help of wired networks or wireless networks to implement a wide variety of application scenarios. The spectrum resources on the other hand become extremely unavailable with the development of communication devices and thereby making it difficult to transmit data on time.

Design/methodology/approach

The spectrum resources on the other hand become extremely unavailable with the development of communication devices and thereby making it difficult to transmit data on time. Therefore, the technology of cognitive radio (CR) is considered as one of the efficient solutions for addressing the drawbacks of spectrum distribution whereas the secondary user (SU) performance is significantly influenced by the spatiotemporal instability of spectrum.

Findings

As a result, the technique of the hybrid filter detection network model (HFDNM) is suggested in this research work under various SU relationships in the networks of CR. Furthermore, a technique of hybrid filter detection was recommended in this work to enhance the performance of idle spectrum applications. When compared to other existing techniques, the suggested research work achieves enhanced efficiency with respect to both throughputs as well as delay.

Originality/value

The proposed HFDNM improved the transmission delay at 3 SUs with 0.004 s/message and 0.008 s/message when compared with existing NCNC and NNC methods in case of number of SUs and also improved 0.02 s/message and 0.08 s/message when compared with the existing methods of NCNC and NNC in case of channel loss probability at 0.3.

Details

International Journal of Intelligent Unmanned Systems, vol. 11 no. 1
Type: Research Article
ISSN: 2049-6427

Keywords

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