Responding to the Call: Exploring Automatic Music Composition Using a Knowledge-Enhanced Model
Zhejing Hu, Yan Liu, Gong Chen, Xiao Ma, Shenghua Zhong, Qianwen Luo
Abstract
Call-and-response is a musical technique that enriches the creativity of music, crafting coherent musical ideas that mirror the back-and-forth nature of human dialogue with distinct musical characteristics. Although this technique is integral to numerous musical compositions, it remains largely uncharted in automatic music composition. To enhance the creativity of machine-composed music, we first introduce the Call-Response Dataset (CRD) containing 19,155 annotated musical pairs and crafted comprehensive objective evaluation metrics for musical assessment. Then, we design a knowledge-enhanced learning-based method to bridge the gap between human and machine creativity. Specifically, we train the composition module using the call-response pairs, supplementing it with musical knowledge in terms of rhythm, melody, and harmony. Our experimental results underscore that our proposed model adeptly produces a wide variety of creative responses for various musical calls.
BibTeX
@article{Hu_Liu_Chen_Ma_Zhong_Luo_2024, title={Responding to the Call: Exploring Automatic Music Composition Using a Knowledge-Enhanced Model}, volume={38}, url={https://ojs.aaai.org/index.php/AAAI/article/view/27807}, DOI={10.1609/aaai.v38i1.27807}, abstractNote={Call-and-response is a musical technique that enriches the creativity of music, crafting coherent musical ideas that mirror the back-and-forth nature of human dialogue with distinct musical characteristics. Although this technique is integral to numerous musical compositions, it remains largely uncharted in automatic music composition. To enhance the creativity of machine-composed music, we first introduce the Call-Response Dataset (CRD) containing 19,155 annotated musical pairs and crafted comprehensive objective evaluation metrics for musical assessment. Then, we design a knowledge-enhanced learning-based method to bridge the gap between human and machine creativity. Specifically, we train the composition module using the call-response pairs, supplementing it with musical knowledge in terms of rhythm, melody, and harmony. Our experimental results underscore that our proposed model adeptly produces a wide variety of creative responses for various musical calls.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Hu, Zhejing and Liu, Yan and Chen, Gong and Ma, Xiao and Zhong, Shenghua and Luo, Qianwen}, year={2024}, month={Mar.}, pages={521-529} }