A Review of Automatic Rhythm Description Systems
Computer Music Journal
A Large-Scale Evaluation of Acoustic and Subjective Music-Similarity Measures
Computer Music Journal
Predicting success from music sales data: a statistical and adaptive approach
Proceedings of the 1st ACM workshop on Audio and music computing multimedia
CAMEO - camera, audio and motion with emotion orchestration for immersive cinematography
ACE '08 Proceedings of the 2008 International Conference on Advances in Computer Entertainment Technology
Compact representation of multimedia files for indexing, classification and retrieval
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
A music recommendation system based on semantic audio segments similarity
EuroIMSA '08 Proceedings of the IASTED International Conference on Internet and Multimedia Systems and Applications
Music recommendation by unified hypergraph: combining social media information and music content
Proceedings of the international conference on Multimedia
Using rich social media information for music recommendation via hypergraph model
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) - Special section on ACM multimedia 2010 best paper candidates, and issue on social media
A context-aware music recommendation system using fuzzy bayesian networks with utility theory
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Machine Learning-Based Adaptive Wireless Interval Training Guidance System
Mobile Networks and Applications
Taxonomy-Oriented recommendation towards recommendation with stage
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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We present the MusicSurfer, a metadata free system for the interaction with massive collections of music. MusicSurfer automatically extracts descriptions related to instrumentation, rhythm and harmony from music audio signals. Together with efficient similarity metrics, the descriptions allow navigation of multimillion track music collections in a flexible and efficient way without the need for metadata nor human ratings.