  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.
