Co-regulated timing in a music ensemble rests on the human capacity to coordinate actions in time. Here we explore the hypothesis that humans predict timing constancy in coordinated actions, in view of timing their own actions in line with the others. An algorithm (BListener) is presented that predicts timing constancy, using Bayesian inference about incoming timing data from the music ensemble. The algorithm is then applied to a timing analysis of real data, first, to a choir consisting of four singers, then, to a dataset containing performances of duet singers. Global features of timing constancy, such as fluctuation and stability, correlate with human subjective estimates of the music ensembles’ quality and associated experienced agency. In future work, BListener could serve as component in an artificial musician that plays along with human musicians in a music ensemble.

音乐合奏中的共同调节时间取决于人类协调及时动作的能力。在这里，我们探索一种假设，即人们根据自己的动作与其他动作的时间同步来预测协调动作中的时间常数。提出了一种算法（BListener），该算法使用关于来自音乐合奏的传入定时数据的贝叶斯推断来预测定时恒定性。然后将该算法应用于真实数据的时序分析，首先应用于由四个歌手组成的合唱团，然后应用于包含二重歌手表演的数据集。时序稳定性的全局特征（例如波动和稳定性）与人类对音乐合奏质量和相关经验代理的主观估计相关。在以后的工作中，