Thomas Gossard, Julian Schmalzl, Andreas Ziegler, Andreas Zell
{"title":"Spin Detection Using Racket Bounce Sounds in Table Tennis","authors":"Thomas Gossard, Julian Schmalzl, Andreas Ziegler, Andreas Zell","doi":"arxiv-2409.11760","DOIUrl":null,"url":null,"abstract":"While table tennis players primarily rely on visual cues, sound provides\nvaluable information. The sound generated when the ball strikes the racket can\nassist in predicting the ball's trajectory, especially in determining the spin.\nWhile professional players can distinguish spin through these auditory cues,\nthey often go unnoticed by untrained players. In this paper, we demonstrate\nthat different rackets produce distinct sounds, which can be used to identify\nthe racket type. In addition, we show that the sound generated by the racket\ncan indicate whether spin was applied to the ball, or not. To achieve this, we\ncreated a comprehensive dataset featuring bounce sounds from 10 racket\nconfigurations, each applying various spins to the ball. To achieve millisecond\nlevel temporal accuracy, we first detect high frequency peaks that may\ncorrespond to table tennis ball bounces. We then refine these results using a\nCNN based classifier that accurately predicts both the type of racket used and\nwhether spin was applied.","PeriodicalId":501284,"journal":{"name":"arXiv - EE - Audio and Speech Processing","volume":"47 2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - EE - Audio and Speech Processing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.11760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
While table tennis players primarily rely on visual cues, sound provides
valuable information. The sound generated when the ball strikes the racket can
assist in predicting the ball's trajectory, especially in determining the spin.
While professional players can distinguish spin through these auditory cues,
they often go unnoticed by untrained players. In this paper, we demonstrate
that different rackets produce distinct sounds, which can be used to identify
the racket type. In addition, we show that the sound generated by the racket
can indicate whether spin was applied to the ball, or not. To achieve this, we
created a comprehensive dataset featuring bounce sounds from 10 racket
configurations, each applying various spins to the ball. To achieve millisecond
level temporal accuracy, we first detect high frequency peaks that may
correspond to table tennis ball bounces. We then refine these results using a
CNN based classifier that accurately predicts both the type of racket used and
whether spin was applied.