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 | Audio Mining with emphasis on Music Genre Classification |  | Anders Meng 
 
 |  | Abstract | Audio is an important part of our daily life, basically it increases our impression of the world around us whether this is communication,
 music, danger detection etc.
 Currently the field of Audio Mining, which here includes areas of
 music genre, music recognition / retrieval, playlist generation
 etc. is receiving quite a lot of attention. The first breakthough in
 audio mining was created by MuscleFish in 1996, a simple audio
 retrieval system. With the increasing amount of audio material being
 accessible through the web, e.g. Apple's iTunes (700,000+ songs),
 Sony, Amazon, new methods in searching / retrieving audio effectively
 is needed. Currently, search engines such as e.g. Google, AltaVista
 etc. do not search into audio files, but uses either the textual
 information attached to the audio file or the textual information
 around the audio. Also in the hearing aid industries around the world
 the problem of detecting environments from the input audio is
 researched as to increase the life quality of
 hearing-impaired. Basically there is a lot of work within the field of
 audio mining.
 
 The presentation will mainly focus on music genre classification where
 we have a fixed amount of genres to choose from. Basically every audio
 mining system is more or less consisting of the same stages as for the
 music genre setting. My research so far has mainly focussed on finding
 relevant features for music genre classification living at different
 timescales using early and late information fusion. It has been found
 that for the task of music genre classification, the features, and
 their temporal relationships are very important when determining the
 music genre.
 |  | Keywords | Audio Mining, Feature extraction, Classification, Eearly / Late information fusion |  | Type | Misc [Presentation] |  | Year | 2004 |  | Note | A presentation as to introduce some of my work at IMM to the ISIS group in Southampton. |  | Electronic version(s) | [pdf]    [zip] |  | BibTeX data | [bibtex] |  | IMM Group(s) | Intelligent Signal Processing | 
 
 
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