Personalized Popular Music Generation Using Imitation and Structure
This work addresses the need for controllable and structured music generation, particularly for applications like music therapy, though it appears incremental in improving upon existing deep learning methods.
The paper tackles the problem of generating personalized pop music by imitating the structure and style of a given seed song, using a statistical machine learning model, and reports that it creates high-quality stylistic music similar to the input based on an evaluation with 10 pop songs.
Many practices have been presented in music generation recently. While stylistic music generation using deep learning techniques has became the main stream, these models still struggle to generate music with high musicality, different levels of music structure, and controllability. In addition, more application scenarios such as music therapy require imitating more specific musical styles from a few given music examples, rather than capturing the overall genre style of a large data corpus. To address requirements that challenge current deep learning methods, we propose a statistical machine learning model that is able to capture and imitate the structure, melody, chord, and bass style from a given example seed song. An evaluation using 10 pop songs shows that our new representations and methods are able to create high-quality stylistic music that is similar to a given input song. We also discuss potential uses of our approach in music evaluation and music therapy.