Multimedia information retrieval: music and audio

Abstract

Music is an omnipresent topic in our daily lives, as almost everyone enjoys listening to his or her favorite tunes. Music information retrieval (MIR) is a research field that aims – among other things – at automatically extracting semantically meaningful information from various representations of music entities, such as a digital audio file, a band’s web page, a song’s lyrics, or a tweet about a microblogger’s current listening activity. A key approach in MIR is to describe music via computational features, which can be categorized into: music content, music context, and user context. The music content refers to features extracted from the audio signal, while information about musical entities not encoded in the signal (e.g., image of an artist or political background of a song) are referred to as music context. The user context, in contrast, includes environmental aspects as well as physical and mental activities of the music listener. MIR research has been seeing a paradigm shift over the last couple of years, as an increasing number of recent approaches and commercial technologies combine content-based techniques (focusing on the audio signal) with multimedia context data mined, e.g. from web sources and with user context information.