DocumentCode :
3568947
Title :
Am-fm modulation features for music instrument signal analysis and recognition
Author :
Zlatintsi, Athanasia ; Maragos, Petros
Author_Institution :
Sch. of Electr. & Comp. Enginr., Nat. Tech. Univ. of Athens, Athens, Greece
fYear :
2012
Firstpage :
2035
Lastpage :
2039
Abstract :
In this paper, we explore a nonlinear AM-FM model to extract alternative features for music instrument recognition tasks. Amplitude and frequency micro-modulations are measured in musical signals and are employed to model the existing information. The features used are the multiband mean instantaneous amplitude (mean-IAM) and mean instantaneous frequency (mean-IFM) modulation. The instantaneous features are estimated using the multiband Gabor Energy Separation Algorithm (Gabor-ESA). An alternative method, the iterativeESA is also explored; and initial experimentation shows that it could be used to estimate the harmonic content of a tone. The Gabor-ESA is evaluated against and in combination with Mel frequency cepstrum coefficients (MFCCs) using both static and dynamic classifiers. The method used in this paper has proven to be able to extract the fine-structured modulations of music signals; further, it has shown to be promising for recognition tasks accomplishing an error rate reduction up to 60% for the best recognition case combined with MFCCs.
Keywords :
Gabor filters; amplitude modulation; feature extraction; frequency modulation; musical instruments; signal classification; source separation; AM-FM modulation feature; Gabor-ESA; MFCC; Mel frequency cepstrum coefficient; alternative feature extraction; amplitude micromodulation; dynamic classifier; error rate reduction; fine-structured modulation; frequency micromodulation; harmonic content estimation; instantaneous feature estimation; iterative ESA; iterative energy separation algorithm; mean instantaneous frequency modulation; multiband Gabor energy separation algorithm; multiband mean instantaneous amplitude modulation; music instrument recognition task; music instrument signal analysis; music instrument signal recognition; nonlinear AM-FM model; static classifier; Feature extraction; Frequency estimation; Frequency modulation; Instruments; Mel frequency cepstral coefficient; Speech; AM-FM modulations; energy separation algorithm; music processing; timbre classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
ISSN :
2219-5491
Print_ISBN :
978-1-4673-1068-0
Type :
conf
Filename :
6334135
Link To Document :
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