标题： JEPOO：用于音乐信息检索的高精度音高、起始和终止联合估计 显示英文标题

标题： JEPOO: Highly Accurate Joint Estimation of Pitch, Onset and Offset for Music Information Retrieval

摘要： 旋律提取是音乐信息检索中的核心任务，而音高、起始点和终止点的估计是旋律提取的关键子任务。现有方法的准确性有限，并且只能处理单一音高或复音高的某一种数据类型。本文提出了一种名为JEPOO的高度准确的方法，用于联合估计音高、起始点和终止点。我们通过新颖的模型设计以及一种名为帕累托调节损失（带有损失权重正则化的）的新优化技术，解决了联合学习优化以及处理单一音高和复音高数据的挑战。这是首个能够准确处理单一音高和复音高音乐数据（甚至包括它们的混合）的方法。广泛的真实数据集上的综合实验研究表明，JEPOO在预测音高、起始点和终止点方面分别比最先进的方法高出10.6%、8.3%和10.3%，并且JEPOO对各种类型的数据和乐器都具有鲁棒性。消融研究显示了JEPOO各组成部分的有效性。 显示英文摘要

摘要： Melody extraction is a core task in music information retrieval, and the estimation of pitch, onset and offset are key sub-tasks in melody extraction. Existing methods have limited accuracy, and work for only one type of data, either single-pitch or multipitch. In this paper, we propose a highly accurate method for joint estimation of pitch, onset and offset, named JEPOO. We address the challenges of joint learning optimization and handling both single-pitch and multi-pitch data through novel model design and a new optimization technique named Pareto modulated loss with loss weight regularization. This is the first method that can accurately handle both single-pitch and multi-pitch music data, and even a mix of them. A comprehensive experimental study on a wide range of real datasets shows that JEPOO outperforms state-ofthe-art methods by up to 10.6%, 8.3% and 10.3% for the prediction of Pitch, Onset and Offset, respectively, and JEPOO is robust for various types of data and instruments. The ablation study shows the effectiveness of each component of JEPOO.