From Sound to "Sense" via Feature Extraction and Machine Learning: Derieving High-level Descriptors for Characterising Music

Research in intelligent music processing is experiencing an enormous boost these days due to the emergence of the new application and research field of Music Information Retrieval (MIR). The rapid growth of digital music collections and the concomitant shift of the music market towards digital music distribution urgently call for intelligent computational support in the automated handling of large amounts of digital music. Ideas for a large variety of content-based music services are currently being developed in music industry and in the research community. They range from content-based music search engines to automatic music recommendation services, from intuitive interfaces on portable music players to methods for the automatic structuring and visualisation of large digital music collections, and from personalised radio stations to tools that permit the listener to actively modify and `play with' the music as it is being played. What all of these content-based services have in common is that they require the computer to be able to `make sense of' and `understand' the actual content of the music, in the sense of being able to recognise and extract musically, perceptually and contextually meaningful (`semantic') patterns from recordings, and to associate descriptors with the music that make sense to human listeners.