Am-fm modulation features for music instrument signal analysis and recognition

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.