Turkish Journal of Electrical Engineering and Computer Sciences




Umpire decisions can greatly affect the outcome of a cricket game. When there is doubt about the umpire?s call for a decision, a decision review system (DRS) may be brought into play by a batsman or bowler to validate the decision. Recently, the latest technologies, including Hotspot, Hawk-eye, and Snickometer, have been employed when there is doubt among the on-field umpire, batsman, or bowlers. This research is a step forward in gaging the true class of a snick generated from the contact of the cricket ball with either (i) the bat, (ii) gloves, (iii) pad, or (iv) a combination of bat and pad. Preprocessing included noise removal from the snick audios using the Audacity program. Machine learning-based classification is achieved by training neural networks with audio features of the snick. A support vector machine was employed to enhance the classification system. Twenty-one features comprising time and frequency domain characteristics were compiled. After one-way analysis of variance was employed and multicomparison analysis was performed, a set of seventeen features was selected and utilized to increase the accuracy of the proposed DRS. The system was trained on indigenous snick data gathered by ourselves and then tested for real cricket snick scenarios. The system achieves a classification rate of 98.3 percent for the self-collected data while presenting an accuracy of 85.7 percentage for the real cricket snick scenarios. The research also developed a dataset of snick audios comprising 132 signals for the four categories to aid researchers working in the same field.


Cricket, snick detection, classification, neural networks, support vector machine, decision review system, audio processing, Snickometer, Hotspot, Hawk-eye, Ultra-edge

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