A Probabilistic Model to Combine Tags and Acoustic Similarity for Music Retrieval
ACM Transactions on Information Systems (TOIS)
Modeling concept dynamics for large scale music search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Hybrid retrieval approaches to geospatial music recommendation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Location-aware music recommendation using auto-tagging and hybrid matching
Proceedings of the 7th ACM conference on Recommender systems
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Many state-of-the-art systems for automatic music tagging model music based on bag-of-features representations which give little or no account of temporal dynamics, a key characteristic of the audio signal. We describe a novel approach to automatic music annotation and retrieval that captures temporal (e.g., rhythmical) aspects as well as timbral content. The proposed approach leverages a recently proposed song model that is based on a generative time series model of the musical content-the dynamic texture mixture (DTM) model-that treats fragments of audio as the output of a linear dynamical system. To model characteristic temporal dynamics and timbral content at the tag level, a novel, efficient, and hierarchical expectation-maximization (EM) algorithm for DTM (HEM-DTM) is used to summarize the common information shared by DTMs modeling individual songs associated with a tag. Experiments show learning the semantics of music benefits from modeling temporal dynamics.