Incorporated in 2015, Musicanote previously developed a music search engine that allows people to enter musical notes and find all scores containing similar melodies. In addition to its online annotation service, Musicanote also offers online music lessons that help students learn how to perform music by giving them access to interpretations of specific pieces of music made by professional musicians and college music professors.
On their own scores, musicians often write their interpretations of the music, suggest performance methods and techniques, and provide other details they feel are necessary for musicians to properly play their music. In addition, musicologists and researchers often make notes regarding the characteristics, significance, and sources of music. The new Musicanote service enables text, video clips and pictures to be attached as annotations to specific measures of music scores and makes them searchable on the internet.
Musicanote provides a space for storing and sharing valuable music-related information and facilitates the searching of such information on the internet. Music performers and researchers can easily record and manage such knowledge online, and music lovers and students can review their notes to acquire more in-depth knowledge of the music they are interested in.
"Recognizing that the range of information which can be found via search engines using keywords related to classical music is limited, we sought to address this shortcoming with our new online annotation service, which enables users to record and retrieve musical knowledge easily. With this service, users are able to access content that is hard to find in books and music scores, exchange opinions on music scores with experts, build up their knowledge and broaden their horizons by reviewing annotations written by famous musicians," explains Song Jaehyun, CEO of Musicanote.
Musicanote plans to improve its music search engine by linking search results with relevant measures when users search for a specific keyword or melody as well as developing an AI function of music based on analyses of annotation data and music scores.
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