Mathematical modelling and a greedy heuristic for harmonic mixing and popularity-based playlist generation problem

Abstract

The rise of the electronic music industry has led to a need for creative playlist-generation methods, particularly for DJs aiming to deliver seamless and harmonically enhanced performances. Harmonic mixing, a crucial process of DJing, involves synchronizing and aligning songs based on musical harmony, making the mix sound soft and clear. In harmonic mixing, the DJ has to select songs from the extensive music archive, considering notes, tempo, length, and popularity of the songs. However, manually generating playlists that adhere to harmonic mixing principles can be time-consuming. This paper introduces a mixed-integer mathematical model and a novel greedy heuristic to automate playlist generation, considering factors like popularity, tempo, and harmonic mixing rules. We compare the novel greedy heuristic's performance to the mathematical model on 16 test problems created with Spotify's API, incorporating real-world data on song characteristics. The results show that the heuristic method generates playlists at least seven times faster and has an average gap of 13.84% with the mathematical model.

Keywords

• Harmonic mixing
• Playlist generation
• Greedy algorithm

References

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