Cognition Guided Interoperability beTween Collections of musical Heritage
10 / 2011 - 10 / 2015
Sound & Vision (S&V) possesses a unique collection of popular Dutch music. The Meertens Institute (MI) possesses a unique collection of Dutch folk songs. These two collections of musical heritage belong to the same culture, but are only separated for institutional reasons. S&V wishes to make these musical archives accessible in an integrated way for the general public. MI wishes the same, to enable musicological research on song evolution. Driven by these demands, COGITCHs general objective is to develop generic techniques to index distributed sources by developing an interoperable system. In a collaborative research, cutting across the boundaries between music cognition and computer science, we develop generic techniques for relating music from different collections. In developing retrieval methods, we will take a top-down approach, working from musical knowledge and cognitive psychology towards the identification and processing of audio features. On-line annotations provided by listeners will support establishing the relationships between hooks (perceptually salient musical patterns) and music. COGITCH involves three intertwined strands of research objectives: " We design and develop a novel infrastructure to let listeners collectively provide annotations and to derive cognitively relevant features. " Based on these cognitively relevant features we invent and implement new music thumbnail extractors and music similarity methods. " Using content-based retrieval methods, a generic interoperable search infrastructure is designed and implemented to access the collections of S&V and MI. The practical results of COGITCH are an interoperable search infrastructure and a workflow for music thumbnails extraction. These are beneficial to S&V and MI, and generally to institutions and industry that preserve collections of musical audio. The scientific results are models of music cognition, cognition based similarity measures, and ground truth data. These enable future music cognition research, musicological research, and music retrieval benchmarking.