Multimodal music information processing and retrieval: survey and future challenges
It addresses the problem of improving music computing applications for researchers and practitioners by synthesizing existing work, but it is incremental as it surveys rather than introduces new methods.
This paper reviews multimodal approaches in music information processing and retrieval, categorizing literature by application and analyzing fusion methods to identify future challenges for the research community.
Towards improving the performance in various music information processing tasks, recent studies exploit different modalities able to capture diverse aspects of music. Such modalities include audio recordings, symbolic music scores, mid-level representations, motion, and gestural data, video recordings, editorial or cultural tags, lyrics and album cover arts. This paper critically reviews the various approaches adopted in Music Information Processing and Retrieval and highlights how multimodal algorithms can help Music Computing applications. First, we categorize the related literature based on the application they address. Subsequently, we analyze existing information fusion approaches, and we conclude with the set of challenges that Music Information Retrieval and Sound and Music Computing research communities should focus in the next years.