Katherine Ellis, E. Coviello, Antoni B. Chan, Gert R. G. Lanckriet
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A Bag of Systems Representation for Music Auto-Tagging
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.
期刊介绍:
The IEEE Transactions on Audio, Speech and Language Processing covers the sciences, technologies and applications relating to the analysis, coding, enhancement, recognition and synthesis of audio, music, speech and language. In particular, audio processing also covers auditory modeling, acoustic modeling and source separation. Speech processing also covers speech production and perception, adaptation, lexical modeling and speaker recognition. Language processing also covers spoken language understanding, translation, summarization, mining, general language modeling, as well as spoken dialog systems.