Reproducing Musicality: Detecting Musical Objects and Emulating Musicality Through Partial Evolution
Document Type
Conference Proceeding
Publication Date
2-2019
Abstract
Musicology is a growing focus in computer science. Past research has had success in automatically generating music through learning-based agents [1] that make use of neural networks and through model and rule-based approaches [2]. These methods require a significant amount of information, either in the form of a large dataset for learning or a comprehensive set of rules based on musical concepts. This paper explores a model in which a minimal amount of musical information is needed to compose a desired style of music. This paper makes use of objectness, a concept directly derived from imagery and pattern recognition to extract specific musical objects from a single musical piece. This is then used as the foundation to produce a new generated musical piece that is similar in style to the original. The overall musical piece is generated through a partial evolution. This method eliminates the need for a large amount of pre-provided data and directly composes music based on a singular source piece.
Recommended Citation
Samson, A. V., & Coronel, A. D. (2019). Reproducing musicality: Detecting musical objects and emulating musicality through partial evolution. 2019 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 014–018. https://doi.org/10.1109/ICAIIC.2019.8669033
