DocumentCode :
1943633
Title :
Musical instrument recognition using ICA-based transform of features and discriminatively trained HMMs
Author :
Eronen, Antti
Author_Institution :
Inst. of Signal Process., Tampere Univ. of Technol., Finland
Volume :
2
fYear :
2003
fDate :
1-4 July 2003
Firstpage :
133
Abstract :
In this paper, we describe a system for the recognition of musical instruments from isolated notes or drum samples. We first describe a baseline system that uses mel-frequency cepstral coefficients and their first derivatives as features, and continuous-density hidden Markov models (HMMs). Two improvements are proposed to increase the performance of this baseline system. First, transforming the features to a base with maximal statistical independence using independent component analysis can give an improvement of 9 percentage points in recognition accuracy. Secondly, discriminative training is shown to further improve the recognition accuracy of the system. The evaluation material consists of 5895 isolated notes of Western orchestral instruments, and 1798 drum hits.
Keywords :
audio signal processing; hidden Markov models; independent component analysis; musical instruments; HMM; ICA-based transform; baseline system; discriminative training; hidden Markov models; independent component analysis; musical instrument recognition; Cepstral analysis; Cepstrum; Feature extraction; Hidden Markov models; Independent component analysis; Instruments; Mel frequency cepstral coefficient; Spatial databases; Steady-state; Topology;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing and Its Applications, 2003. Proceedings. Seventh International Symposium on
Print_ISBN :
0-7803-7946-2
Type :
conf
DOI :
10.1109/ISSPA.2003.1224833
Filename :
1224833
Link To Document :
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