The rise of digital music has led to a parallel rise in the need to manage music collections of several thousands of songs on a single device. Manual selection of songs for a music listening experience is a cumbersome task. In this paper, we present an initial exploration of the feasibility of using song signal properties and user context information to assist in automatic song selection. Users listened to music over the course of a month while their context and song selections were tracked. Initial results suggest the use of context information can improve automated song selection when patterns are learned for each individual.

Audio signal properties can provide a media player with highly descriptive feature sets in order to intelligently select similar songs for a music stream. A well-known problem among researchers in music information retrieval, however, is that extracting signal properties requires a significant amount of computational resources, thus making it impractical for even the most advanced mobile media players. Although other approaches to retrieving data are possible, local extraction still has unique benefits. Using a combination of machine learning and profiling techniques, this paper presents an initial evaluation of partial signal extraction, which reduces resource requirements by locally collecting signals from parts of a song rather than all. Our preliminary experiments suggest that this idea can offer significantly lower resource requirements while losing marginal song information.