JEPOO: Highly Accurate Joint Estimation of Pitch, Onset and Offset for Music Information Retrieval
IJCAI 2023(2023)
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
Offset, respectively, and JEPOO is robust for various types of data and
instruments. The ablation study shows the effectiveness of each component of
JEPOO.
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