  Commonly music has an obvious hierarchical structure, especially for the
singing parts which usually act as the main melody in pop songs. However, most
of the current singing annotation datasets only record symbolic information of
music notes, ignoring the structure of music. In this paper, we propose a
hierarchical singing annotation dataset that consists of 68 pop songs from
Youtube. This dataset records the onset/offset time, pitch, duration, and lyric
of each musical note in an enhanced LyRiCs format to present the hierarchical
structure of music. We annotate each song in a two-stage process: first, create
initial labels with the corresponding musical notation and lyrics file; second,
manually calibrate these labels referring to the raw audio. We mainly validate
the labeling accuracy of the proposed dataset by comparing it with an automatic
singing transcription (AST) dataset. The result indicates that the proposed
dataset reaches the labeling accuracy of AST datasets.
