We've read a paper about Speech/Music separation - "Frame-Level Speech/Music Discrimination using AdaBoost." Because of the powerful feature selection ability of AdaBoost, We think the algorithm introduced in this paper can be exploitted to do other classifactions too. For example, pure music and music with vocal.
Therefore, the first step we're going to do is implementing the audio classfication by AdaBoost. Some difficutly we might face:
- Mannual Annotation - a very boring process
- AdaBoost needs much training time - which means we don't have many chances to test
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toolkit is here:
http://www.iro.umontreal.ca/~casagran/multiboost.html#
should this be a supervised or unsupervised case?
supposedly you don't know or need not know who sings but just try to correlate those singing segments
i think it should be a supervised case in detecting music/speech/others. But in detecting singers, maybe it can be done by unsupervised ways. We will try!!
yap.
i agree in such two stages.
As for music/speech discrimination, there are quite salient work. You might try to google them. or I know the following work is quite cited.
Dan Ellis, "Speech/music discrimination based on posterior probability features"
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