A Bag of Systems Representation for Music Auto-Tagging

Abstract

We present a content-based automatic tagging system for music that relies on a high-level, concise “Bag of Systems” (BoS) representation of the characteristics of a musical piece. The BoS representation leverages a rich dictionary of musical codewords, where each codeword is a generative model that captures timbral and temporal characteristics of music. Songs are represented as a BoS histogram over codewords, which allows for the use of traditional algorithms for text document retrieval to perform auto-tagging. Compared to estimating a single generative model to directly capture the musical characteristics of songs associated with a tag, the BoS approach offers the flexibility to combine different generative models at various time resolutions through the selection of the BoS codewords. Additionally, decoupling the modeling of audio characteristics from the modeling of tag-specific patterns makes BoS a more robust and rich representation of music. Experiments show that this leads to superior auto-tagging performance.