Leveraging Compatibility and Diversity in Computational Music Mashup Creation

In this paper, we advance a multimodal optimization music mashup creation model for loop recombination at scale. The motivation to pursue such a model is to 1) tackle current scalability limitations in state-of-the-art (brute force) models while enforcing the 2) compatibility, i.e., recombination quality, of audio loops, and 3) a pool of diverse solutions that can accommodate personal user preferences or promote different musical styles. To this end, we adopt the Artificial Immune System (AIS) opt-aiNet algorithm to efficiently compute a population of compatible and diverse mashups from loop recombinations. Optimal mashups result from local minima in a feature space that objectively represents harmonic and rhythmic compatibility. We implemented our model as a prototype application named Mixmash-AIS, and conducted an objective evaluation that tackles three dimensions: loop recombination compatibility, mashups diversity, and computational model efficiency. The conducted evaluation compares the proposed system to a standard genetic algorithm (GA) and a brute force (BF) approach. While the GA stands as the most efficient algorithm, its poor results in terms of compatibility reinforce the primacy of the AIS opt-aiNet in efficiently finding optimal compatible loop mashups. Furthermore, the AIS opt-aiNet showed to promote a diverse mashup population, outperforming both GA or BF approaches.