- From: Giovanni Tummarello <g.tummarello@gmail.com>
- Date: Wed, 19 Jul 2006 19:09:47 +0200
- To: MMSem public <public-xg-mmsem@w3.org>
- Message-ID: <44BE675B.9090602@gmail.com>
Attached the music use case named Social Semantic Browsing. We'll really appreciate feedbacks on this scenario. Yell now or help afterwards! :-) Giovanni ---- *Music related use case: social semantic browsing* *Authors: Giovanni Tummarello, Christian Morbidoni* A great number of Internet users have consistent music collection composed of locally playable files in formats such as MP3, Ogg Vorbis etc.. Such files often come from diverse sources such as online shops, file sharing, direct CD ripping, personal recording etc. We assume that such users are connected to the Internet and therefore can access both the “semantic web” and specialized services. As the number of such media is likely to be great, “sensible” browsing using traditional methods (e.g. Picking from a list) is not possible. By “sensible” browsing we mean selecting a “next” piece according to characteristic of the “previous” so that, for example, there might be matches or not strong mismatches in genre, author, timber, rhythm, velocity, instruments, acoustic pressure etc. In order to assist the user (or automatically act, e.g. Compose a playlist, upon such premises) both low level features and higher level metadata are needed. The metadata associated with such audio collections files are heterogeneous in quality, if they exist at all. Metadata associated with such files are usually limited to file names, file system informations (e.g. The names of the folder in which the user has saved the files) and ID3 like information headers. Except for fields like “genre” which is encoded as integer, such information fields are typically encoded as strings which do not, naturally, act as unique identifiers. To address this use case, there is need of using a combination of techniques such as text heuristics combined with low level audio features algorithms to provide fast and reliable identification of the tracks, low level audio features algorithms to extract content descriptions and social semantic web technologies, e.g. Semantic web P2P to retrieve high level metadata once the tracks have been identified.
Attachments
- application/vnd.oasis.opendocument.text attachment: use_case_social_semantic_music.odt
Received on Wednesday, 19 July 2006 17:10:31 UTC