  Melody is one of the most important components in music. Unlike other
components in music theory, such as harmony and counterpoint, computable
features for melody is urgently in need. These features are highly demanded as
data-driven methods dominating the fields such as musical information retrieval
and automatic music composition. To boost the performance of
deep-learning-related musical tasks, we propose a set of interpretable features
on monophonic melody for computational purposes. These features are defined not
only in mathematical form, but also with some considerations on composers
'intuition. For example, the Melodic Center of Gravity can reflect the
sentence-wise contour of the melody, the local / global melody dynamics
quantifies the dynamics of a melody that couples pitch and time in a sentence.
We found that these features are considered by people universally in many
genres of songs, even for atonal composition practices. Hopefully, these
melodic features can provide nov el inspiration for future researchers as a
tool in the field of MIR and automatic composition.

Sound;Artificial Intelligence;Audio and Speech Processing