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Compilation of musical instrument sample databases requires careful elimination of badly recorded samples and validation of sample classification into correct categories. This paper introduces algorithms for automatic removal of bad instrument samples using automatic musical instrument recognition and outlier detection techniques. Best evaluation results on a methodically contaminated sound database are achieved using the introduced MCIQR method, which removes 70.1% "bad" samples with 0.9% false-alarm rate and 90.4% with 8.8% false-alarm rate.
Audio, Speech, and Language Processing, IEEE Transactions on (Volume:17 , Issue: 5 )
Date of Publication: July 2009