Time Series Models for Semantic Music Annotation

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.