Energy-aware adaptation for mobile applications
Proceedings of the seventeenth ACM symposium on Operating systems principles
DJogger: a mobile dynamic music device
CHI '06 Extended Abstracts on Human Factors in Computing Systems
PersonalSoundtrack: context-aware playlists that adapt to user pace
CHI '06 Extended Abstracts on Human Factors in Computing Systems
Lifetrak: music in tune with your life
Proceedings of the 1st ACM international workshop on Human-centered multimedia
A Human Activity Aware Learning Mobile Music Player
Proceedings of the 2007 conference on Advances in Ambient Intelligence
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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.