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.
|Publication status||Published - 2004|
Bibliographical noteA presentation as to introduce some of my work at IMM to the ISIS group in Southampton.
- Audio Mining
- Feature extraction
- Eearly / Late information fusion