Greek folk music classification into two genres using lyrics and audio via canonical correlation analysis

We are interested in Greek folk music genre classification by resorting to canonical correlation analysis (CCA). Here, the genre is related to the place of origin of the song. The CCA learns a linear transformation of the song lyrics descriptors that is highly correlated with their genre labels as well as another linear transformation of the audio features extracted from music recordings, which is maximally correlated with their genre labels. In the latter task, thanks to the deep CCA (DCCA), deep nonlinear transformations of the audio features are learnt, which are maximally correlated with the genre labels. Experimental findings are disclosed for a two-class genre recognition problem, employing folk songs originated from Pontus and Asia Minor. It is demonstrated that the CCA achieves an average accuracy of 97.02% across the 5 folds, when the term frequency-inverse document frequency features model the song lyrics. By modeling the music signal of each song with 28 mel-frequency cepstral coefficients (MFCCs) extracted from each frame and averaged over all frames, the average accuracy of the CCA drops to 72.9% across the 5 folds. The DCCA yields an accuracy of 69% for audio-based genre recognition.