We provide a survey of the field of Music Information Retrieval (MIR), in particular paying attention to latest developments, such as semantic auto-tagging and user-centric retrieval and recommendation approaches. We first elaborate on well-established and proven methods for feature extraction and music indexing, from both the audio signal and contextual data sources about music items, such as web pages or collaborative tags. These in turn enable a wide variety of music retrieval tasks, such as semantic music search or music identification (“query by example”). Subsequently, we review current work on user analysis and modeling in the context of music recommendation and retrieval, addressing the recent trend towards user-centric and adaptive approaches and systems. A discussion follows about the important aspect of how various MIR approaches to different problems are evaluated and compared. Eventually, a discussion about the major open challenges concludes the survey.

This work is supported by the Austrian Science Fund (FWF): P22856 and P25655, as well as by the European Union Seventh Framework Programme FP7 / 2007-2013 through the PHENICX project under grant agreement no. 601166, the A4U postdoctoral grants programme, and the Junta of Andalusia through the COFLA2 project (P12-TIC-1362). The authors would further like to thank Masataka Goto, Mohamed Sordo, and Edith Law for granting permission to include their visual material in this survey, and Juan J. Bosch for his comments on the manuscript. Furthermore, the authors would like to express their gratitude to the anonymous reviewers for their highly valuable suggestions for improving the manuscript.