Musical genres: beating to the rhythms of different drums

Online music databases have increased significantly as a consequence of the rapid growth of the Internet and digital audio, requiring the development of faster and more efficient tools for music content analysis. Musical genres are widely used to organize music collections. In this paper, the problem of automatic single and multi-label music genre classification is addressed by exploring rhythm-based features obtained from a respective complex network representation. A Markov model is built in order to analyse the temporal sequence of rhythmic notation events. Feature analysis is performed by using two multi-variate statistical approaches: principal components analysis (unsupervised) and linear discriminant analysis (supervised). Similarly, two classifiers are applied in order to identify the category of rhythms: parametric Bayesian classifier under the Gaussian hypothesis (supervised) and agglomerative hierarchical clustering (unsupervised). Qualitative results obtained by using the kappa coefficient and the obtained clusters corroborated the effectiveness of the proposed method.

Pessoa e Pirandello: confluência de dramas

The present study aims to compare two modern authors - Luigi Pirandello and Fernando Pessoa - from different cultural environments - Italy / Brazil - pointing to a common theme: the various levels of the individual's expression. The arguments to support the proposed analysis are based on the shifting literary genres, specifically on the non-dramatic characters by Pirandello and on the drama in people by Pessoa.