Purging Musical Instrument Sample Databases Using Automatic Musical Instrument Recognition Methods

Author

Livshin, Arie ; Rodet, Xavier

Author_Institution

Inst. de Rech. et Coordination Acoust./Musique (IRCAM), Paris

Volume

17

Issue

5

fYear

2009

fDate

7/1/2009 12:00:00 AM

Firstpage

1046

Lastpage

1051

Abstract

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.

Keywords

acoustic signal processing; database management systems; musical instruments; MCIQR method; automatic musical instrument recognition methods; bad instrument sample removal; musical instrument sample databases; outlier detection techniques; sample classification; Fourier transforms; Instruments; Multimedia databases; Music information retrieval; Pattern classification; Pattern recognition; Scholarships; Synthesizers; Testing; Vectors; Instrument recognition; multimedia databases; music; music information retrieval; pattern classification;

fLanguage

English

Journal_Title

Audio, Speech, and Language Processing, IEEE Transactions on

Publisher

ieee

ISSN

1558-7916

Type

jour

DOI

10.1109/TASL.2009.2018439

Filename

5067422