@InProceedings{7760425,
  author = {E. Fotiadou and N. Bassiou and C. Kotropoulos},
  booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},
  title = {Greek folk music classification using auditory cortical representations},
  year = {2016},
  pages = {1133-1137},
  abstract = {In this paper, we deal with the classification of Greek folk songs into 8 classes associated with the region of origin of the songs. Motivated by the way the sound is perceived by the human auditory system, auditory cortical representations are extracted from the music recordings. Moreover, deep canonical correlation analysis (DCCA) is applied to the auditory cortical representations for dimensionality reduction. To classify the music recordings, either support vector machines (SVMs) or classifiers based on canonical correlation are employed. An average classification rate of 73.25 % is measured on a dataset of Greek folk songs from 8 regions, when the auditory cortical representations are classified by the SVMs. It is also demonstrated that the reduced features extracted by the DCCA yield an encouraging average classification rate of 66.27%. The latter features are shown to possess good discriminating properties.},
  keywords = {acoustic correlation;acoustic signal processing;feature extraction;signal classification;signal representation;support vector machines;Greek folk music classification;auditory cortical representation;Greek folk song classification;human auditory system;deep canonical correlation analysis;dimensionality reduction;support vector machine;SVM;canonical correlation;average classification rate;feature extraction;DCCA;Correlation;Time-frequency analysis;Auditory system;Feature extraction;Europe;Signal processing;Music},
  doi = {10.1109/EUSIPCO.2016.7760425},
  issn = {2076-1465},
  month = {Aug},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2016/papers/1570252053.pdf},
}

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Motivated by the way the sound is perceived by the human auditory system, auditory cortical representations are extracted from the music recordings. Moreover, deep canonical correlation analysis (DCCA) is applied to the auditory cortical representations for dimensionality reduction. To classify the music recordings, either support vector machines (SVMs) or classifiers based on canonical correlation are employed. An average classification rate of 73.25 % is measured on a dataset of Greek folk songs from 8 regions, when the auditory cortical representations are classified by the SVMs. It is also demonstrated that the reduced features extracted by the DCCA yield an encouraging average classification rate of 66.27%. 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Kotropoulos},\n booktitle = {2016 24th European Signal Processing Conference (EUSIPCO)},\n title = {Greek folk music classification using auditory cortical representations},\n year = {2016},\n pages = {1133-1137},\n abstract = {In this paper, we deal with the classification of Greek folk songs into 8 classes associated with the region of origin of the songs. Motivated by the way the sound is perceived by the human auditory system, auditory cortical representations are extracted from the music recordings. Moreover, deep canonical correlation analysis (DCCA) is applied to the auditory cortical representations for dimensionality reduction. To classify the music recordings, either support vector machines (SVMs) or classifiers based on canonical correlation are employed. An average classification rate of 73.25 % is measured on a dataset of Greek folk songs from 8 regions, when the auditory cortical representations are classified by the SVMs. It is also demonstrated that the reduced features extracted by the DCCA yield an encouraging average classification rate of 66.27%. 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