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. |
2013 |
A Zlatintsi, P Maragos Multiscale Fractal Analysis of Musical Instrument Signals with Application to Recognition Journal Article 21 (4), pp. 737–748, 2013. Abstract | BibTeX | Links: [PDF] @article{ZlMa13, title = {Multiscale Fractal Analysis of Musical Instrument Signals with Application to Recognition}, author = {A Zlatintsi and P Maragos}, url = {http://robotics.ntua.gr/wp-content/publications/ZlatintsiMaragos_MultiscaleFractalAnalMusicInstrumSignalsApplicRecogn_ieeetASLP2013.pdf}, year = {2013}, date = {2013-04-01}, volume = {21}, number = {4}, pages = {737--748}, abstract = {In this paper, we explore nonlinear methods, inspired by the fractal theory for the analysis of the structure of music signals at multiple time scales, which is of importance both for their modeling and for their automatic computer-based recognition. We propose the multiscale fractal dimension (MFD) prourl as a short-time descriptor, useful to quantify the multiscale complexity and fragmentation of the different states of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments, which is verified by further analysis on synthesized sinusoidal signals. We compare the descriptiveness of our features against that of Mel frequency cepstral coefficients (MFCCs), using both static and dynamic classifiers such as Gaussian mixture models (GMMs) and hidden Markov models (HMMs). The method and features proposed in this paper appear to be promising for music signal analysis, due to their capability for multiscale analysis of the signals and their applicability in recognition, as they accomplish an error reduction of up to 32%. These results are quite interesting and render the descriptor of direct applicability in large-scale music classification tasks.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this paper, we explore nonlinear methods, inspired by the fractal theory for the analysis of the structure of music signals at multiple time scales, which is of importance both for their modeling and for their automatic computer-based recognition. We propose the multiscale fractal dimension (MFD) prourl as a short-time descriptor, useful to quantify the multiscale complexity and fragmentation of the different states of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments, which is verified by further analysis on synthesized sinusoidal signals. We compare the descriptiveness of our features against that of Mel frequency cepstral coefficients (MFCCs), using both static and dynamic classifiers such as Gaussian mixture models (GMMs) and hidden Markov models (HMMs). The method and features proposed in this paper appear to be promising for music signal analysis, due to their capability for multiscale analysis of the signals and their applicability in recognition, as they accomplish an error reduction of up to 32%. These results are quite interesting and render the descriptor of direct applicability in large-scale music classification tasks. |
Athanasia Zlatintsi, Petros Maragos Multiscale fractal analysis of musical instrument signals with application to recognition Journal Article IEEE Transactions on Audio, Speech and Language Processing, 21 (4), pp. 737–748, 2013, ISSN: 15587916. Abstract | BibTeX | Links: [PDF] @article{140, title = {Multiscale fractal analysis of musical instrument signals with application to recognition}, author = {Athanasia Zlatintsi and Petros Maragos}, url = {http://robotics.ntua.gr/wp-content/uploads/publications/ZlatintsiMaragos_MultiscaleFractalAnalMusicInstrumSignalsApplicRecogn_ieeetASLP2013.pdf}, doi = {10.1109/TASL.2012.2231073}, issn = {15587916}, year = {2013}, date = {2013-01-01}, journal = {IEEE Transactions on Audio, Speech and Language Processing}, volume = {21}, number = {4}, pages = {737--748}, abstract = {—In this paper, we explore nonlinear methods, inspired by the fractal theory for the analysis of the structure of music sig- nals at multiple time scales, which is of importance both for their modeling and for their automatic computer-based recognition.We propose the multiscale fractal dimension (MFD) profile as a short- time descriptor, useful to quantify the multiscale complexity and fragmentation of the different states of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments, which is veri- fied by further analysis on synthesized sinusoidal signals.We com- pare the descriptiveness of our features against that of Mel fre- quency cepstral coefficients (MFCCs), using both static and dy- namic classifierssuch asGaussian mixture models (GMMs) and hidden Markov models (HMMs). The method and features pro- posed in this paper appear to be promising for music signal anal- ysis,due to their capability for multiscale analysis of the signals and their applicability in recognition, as they accomplish an error re- duction of up to 32%.These results are quite interesting and render the descriptor of direct applicability in large-scalemusic classifica- tion tasks.}, keywords = {}, pubstate = {published}, tppubtype = {article} } —In this paper, we explore nonlinear methods, inspired by the fractal theory for the analysis of the structure of music sig- nals at multiple time scales, which is of importance both for their modeling and for their automatic computer-based recognition.We propose the multiscale fractal dimension (MFD) profile as a short- time descriptor, useful to quantify the multiscale complexity and fragmentation of the different states of the music waveform. We have experimentally found that this descriptor can discriminate several aspects among different music instruments, which is veri- fied by further analysis on synthesized sinusoidal signals.We com- pare the descriptiveness of our features against that of Mel fre- quency cepstral coefficients (MFCCs), using both static and dy- namic classifierssuch asGaussian mixture models (GMMs) and hidden Markov models (HMMs). The method and features pro- posed in this paper appear to be promising for music signal anal- ysis,due to their capability for multiscale analysis of the signals and their applicability in recognition, as they accomplish an error re- duction of up to 32%.These results are quite interesting and render the descriptor of direct applicability in large-scalemusic classifica- tion tasks. |
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