预测性别与击球成功率--网球比赛中选手咕噜声的计算机辅助分析
Predicting Sex and Stroke Success -- Computer-aided Player Grunt Analysis in Tennis Matches
摘要：
专业运动员越来越多地使用Meta和信号数据的自动分析来提高他们的训练和比赛表现。与其他相关的人对人研究领域一样，信号数据尤其包含用于自动分析的重要性能和情绪特定指标。在本文中，我们介绍了新的数据集SCORE！研究几个特征和机器学习范例在网球比赛中预测性别和即时击球成功率的性能，仅基于通过球员的咕噜声的声音表达。这些数据是从YouTube上收集的，并以完全相同的定义进行标记，并对音频进行建模处理。我们提取了音频样本的几个广泛使用的基本，专家知识和深层声学特征，并结合各种机器学习方法评估其有效性。在二进制设置中，最好的系统，使用频谱图和卷积递归神经网络，实现了球员性别预测任务的未加权平均召回率（UAR）为84.0%，预测击球成功率为60.3%，仅基于两种性别球员的咕噜声中的声学线索。此外，当模型只训练女性或男性咕噜声时，我们分别实现了58.3%和61.3%的UAR。
英文摘要：
Professional athletes increasingly use automated analysis of meta- and signal data to improve their training and game performance. As in other related human-to-human research fields, signal data, in particular, contain important performance- and mood-specific indicators for automated analysis. In this paper, we introduce the novel data set SCORE! to investigate the performance of several features and machine learning paradigms in the prediction of the sex and immediate stroke success in tennis matches, based only on vocal expression through players' grunts. The data was gathered from YouTube, labelled under the exact same definition, and the audio processed for modelling. We extract several widely used basic, expert-knowledge, and deep acoustic features of the audio samples and evaluate their effectiveness in combination with various machine learning approaches. In a binary setting, the best system, using spectrograms and a Convolutional Recurrent Neural Network, achieves an unweighted average recall (UAR) of 84.0 % for the player sex prediction task, and 60.3 % predicting stroke success, based only on acoustic cues in players' grunts of both sexes. Further, we achieve a UAR of 58.3 %, and 61.3 %, when the models are exclusively trained on female or male grunts, respectively.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
