Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions
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.
Cite
@article{arxiv.1606.02096,
title = {Towards Playlist Generation Algorithms Using RNNs Trained on Within-Track Transitions},
author = {Keunwoo Choi and George Fazekas and Mark Sandler},
journal= {arXiv preprint arXiv:1606.02096},
year = {2016}
}
Comments
4 pages, 2 figures, accepted to Workshop on Surprise, Opposition, and Obstruction in Adaptive and Personalized Systems (SOAP) 2016, Halifax, Canada