DJ AI: optimizing playlist alignment and generating transitions with generative and embedding models

Digital music platforms have transformed the listening experience through curated playlists and transitions, yet many transition creating systems primarily serve professional DJs with many manual features to be set while overlooking amateur performers and everyday listeners. In this study, we introduce DJ-AI, a novel framework that bridges this gap by analyzing detailed musical features to optimize song sequences and create harmonic transitions between those songs. Our approach employs graph based optimization techniques to efficiently arrange playlists by mapping song relationships and determining the best transition paths. Additionally, we integrate MusicGen—a generative model for generating coherent musical continuations—and MERT audio embedding model, which capture nuanced musical attributes, to enhance the smoothness of transitions. Experimental evaluations reveal that DJ-AI outperforms traditional crossfade methods in generating smooth and coherent transitions. This framework paves the way for AI-driven adaptive mixing solutions, making seamless music transitions more accessible to a broader audience.