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The work undertaken is a comprehensive analysis of cricket sounds, focusing on the interaction of the ball with the bat and the wicket, the study aims to distinguish between edged, shot, and bowled audio in both noisy and noise-free environments. Upon feature extraction, machine learning models XGBoost and Random Forest were trained, to accurately classify these distinct cricketing events. This not only enriches the realm of cricket analysis by facilitating informed decision- making and insights into player performance but also showcases the potential of audio-based sports analytics.

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Analysis of the Audio in the Game of Cricket Using Machine Learning

  • 会议论文

  • 发表日期：

    2026年

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  • M. V. Varun,
  • A. H. Venkat Raghavendra,
  • V. Hemanth,
  • Ashwini Bhat

摘要

The work undertaken is a comprehensive analysis of cricket sounds, focusing on the interaction of the ball with the bat and the wicket, the study aims to distinguish between edged, shot, and bowled audio in both noisy and noise-free environments. Upon feature extraction, machine learning models XGBoost and Random Forest were trained, to accurately classify these distinct cricketing events. This not only enriches the realm of cricket analysis by facilitating informed decision- making and insights into player performance but also showcases the potential of audio-based sports analytics.