Automatic Playlist Sequencing and Transitions

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...

Embedding Songs and Tags for Playlist Prediction

Automatic playlist generation can be a useful tool to navigate the myriad choices available to users in music services today. Here, we present our recent work on explicitly modeling playlists without requiring external similarity measures. Our Logistic Markov Embedding is trained directly on historical playlist data and can unify songs and (when available) social tags in a Euclidean space. The ...

Automatic Playlist Generation via Music Mood Analysis

An Extensible Representation for Playlists

The increasing availability of digital music has created a greater need for methods to organize large collections of music. The eXtensible PlayList (XPL) representation allows users to express playlists with varying degrees of specificity. XPL handles references to exact files or URLs as well as rules for selecting content based on metadata constraints. XPL also allows the transitions between t...

Empirical Analysis of Track Selection and Ordering in Electronic Dance Music using Audio Feature Extraction

Disc jockeys are in some ways the ultimate experts at selecting and playing recorded music for an audience, especially in the context of dance music. In this work, we empirically investigate factors affecting track selection and ordering using mixes created for the Essential Mix. The Essential Mix is a well known weekly radio show on BBC Radio 1 that showcases various styles of electronic dance...