2014 |
A Zlatintsi, P Maragos Comparison of Different Representations Based on Nonlinear Features for Music Genre Classification Conference Proc. European Signal Processing Conference, Lisbon, Portugal, 2014. Abstract | BibTeX | Links: [PDF] @conference{ZlMa14, title = {Comparison of Different Representations Based on Nonlinear Features for Music Genre Classification}, author = {A Zlatintsi and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/ZlatintsiMaragos_MGC_EUSIPCO14_Lisbon_proc.pdf}, year = {2014}, date = {2014-09-01}, booktitle = {Proc. European Signal Processing Conference}, address = {Lisbon, Portugal}, abstract = {In this paper, we examine the descriptiveness and recognition properties of different feature representations for the analysis of musical signals, aiming in the exploration of their micro- and macro-structures, for the task of music genre classification. We explore nonlinear methods, such as the AM-FM model and ideas from fractal theory, so as to model the time-varying harmonic structure of musical signals and the geometrical complexity of the music waveform. The different feature representations’ efficacy is compared regarding their recognition properties for the specific task. The proposed features are evaluated against and in combination with Mel frequency cepstral coefficients (MFCC), using both static and dynamic classifiers, accomplishing an error reduction of 28%, illustrating that they can capture important aspects of music.}, keywords = {}, pubstate = {published}, tppubtype = {conference} } In this paper, we examine the descriptiveness and recognition properties of different feature representations for the analysis of musical signals, aiming in the exploration of their micro- and macro-structures, for the task of music genre classification. We explore nonlinear methods, such as the AM-FM model and ideas from fractal theory, so as to model the time-varying harmonic structure of musical signals and the geometrical complexity of the music waveform. The different feature representations’ efficacy is compared regarding their recognition properties for the specific task. The proposed features are evaluated against and in combination with Mel frequency cepstral coefficients (MFCC), using both static and dynamic classifiers, accomplishing an error reduction of 28%, illustrating that they can capture important aspects of music. |
2012 |
A Zlatintsi, P Maragos AM-FM Modulation Features for Music Instrument Signal Analysis and Recognition Conference Proc. European Signal Processing Conference, Bucharest, Romania, 2012. Abstract | BibTeX | Links: [PDF] @conference{ZlMa12, title = {AM-FM Modulation Features for Music Instrument Signal Analysis and Recognition}, author = {A Zlatintsi and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/ZlatintsiMaragos_MusicalInstrumentsAMFM_EUSIPCO2012.pdf}, year = {2012}, date = {2012-08-01}, booktitle = {Proc. European Signal Processing Conference}, address = {Bucharest, Romania}, 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 iterative-ESA 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 = {}, pubstate = {published}, tppubtype = {conference} } 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 iterative-ESA 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. |
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