Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

Keunwoo Choi; George Fazekas; Mark Sandler

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arXiv · 1606.02096

Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions

Keunwoo Choi·George Fazekas·Mark Sandler

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Abstract

We introduce a novel playlist generation algorithm that focuses on the quality of transitions using a recurrent neural network (RNN). The proposed model assumes that optimal transitions between tracks can be modelled and predicted by internal transitions within music tracks. We introduce modelling sequences of high-level music descriptors using RNNs and discuss an experiment involving different similarity functions, where the sequences are provided by a musical structural analysis algorithm. Qualitative observations show that the proposed approach can effectively model transitions of music tracks in playlists.

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Keunwoo Choi, George Fazekas, Mark Sandler. 2016-06-07. Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions. https://arxiv.org/abs/1606.02096

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