Exploring Inherent Properties of the Monophonic Melody of Songs
This provides incremental tools for researchers in music information retrieval and automatic composition by addressing a known bottleneck in melody analysis.
The paper tackled the lack of computable features for monophonic melody in music, proposing a set of interpretable features like Melodic Center of Gravity and local/global melody dynamics to enhance deep-learning tasks in musical information retrieval and automatic composition, finding these features are universally considered across many song genres.
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.