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.

翻译：旋律提取是音乐信息检索中的核心任务，而基音、起始点与终止点的估计是旋律提取的关键子任务。现有方法精度有限，且仅适用于单一类型数据（单音或多音）。本文提出一种高精度基音、起始点与终止点联合估计方法，命名为JEPOO。我们通过新颖的模型设计及一种名为Pareto调制损失结合损失权重正则化的优化技术，解决了联合学习优化以及处理单音与多音数据的挑战。这是首个能够精确处理单音、多音音乐数据及其混合数据的方法。在广泛真实数据集上的综合实验研究表明，JEPOO在基音、起始点与终止点预测上分别超越现有最优方法最高达10.6%、8.3%和10.3%，且对多种数据类型与乐器具有鲁棒性。消融实验验证了JEPOO各组件的有效性。