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Random Playlists Smoothly Commuting Between Styles
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DOI：10.1145/3361742.png)
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Abstract
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Someone enjoys listening to playlists while commuting. He wants a different playlist of n songs each day, but always starting from Locked Out of Heaven, a Bruno Mars song. The list should progress in smooth transitions between successive and randomly selected songs until it ends up at Stairway to Heaven, a Led Zeppelin song. The challenge of automatically generating random and heterogeneous playlists is to find the appropriate balance among several conflicting goals. We propose two methods for solving this problem. One is called ROPE, and it depends on a representation of the songs in a Euclidean space. It generates a random path through a Brownian Bridge that connects any two songs selected by the user in this music space. The second is STRAW, which constructs a graph representation of the music space where the nodes are songs and edges connect similar songs. STRAW creates a playlist by traversing the graph through a steering random walk that starts on a selected song and is directed toward a target song also selected by the user. When compared with the state-of-the-art algorithms, our algorithms are the only ones that satisfy the following quality constraints: heterogeneity, smooth transitions, novelty, scalability, and usability. We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs and make available a prototype.
Keywords:
Music
sound and music computing
system applications and experience
knowledge and data engineering tools and techniques
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