Towards Playlist Generation Algorithms Using RNNs Trained on   Within-Track Transitions
  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. 
Keunwoo Choi, and George Fazekas, Mark Sandler
cs.AI cs.MM cs.SD