Search

In this study an Artificial Neural Network called Self-Organizing Map (SOM) is used in order to classify the synoptic circulation over Europe and especially Eastern Mediterranean. The classification of circulation types is an effective way of summarizing and describing the atmospheric circulation and it is useful in climatology because it provides a better understanding of the climatic variability over an area. Here, the SOM methodology is applied on winter daily geopotential height anomalies of the 500 hPa level, for the period 1971–2000. Twelve unique circulation patterns are identified. Eight of these types are characterized as cyclonic, representing 61% of the total days examined and four types are characterized as anticyclonic, representing 39% of the study period. The results of this classification are comparable to other objective classifications applied on the same study region and present a similar image. Therefore, the SOM methodology is found to be applicable and useful in the classification of circulation types. [ABSTRACT FROM AUTHOR]

This paper revisits a mathematical model of motivic analysis, together with its computational realization, from an interdisciplinary perspective, relating it to concepts and methods in the field of computational music analysis. Issues such as segmentation, motivic formation, knowledge representation, similarity and categorization, and interpretation of results are discussed. A further introspection on the approach, in the context of mathematics, computation, and music analysis as a large interdisciplinary field, reveals relations between the three: the mathematical model (motivic topologies), its computational counterpart (OM-Melos), and music analysis (Réti's and Nattiez’ methods). In doing so, we stress the importance of neutrality, objectivity, and scientific rigour in the modelling part while at the same time preserving the freedom of the music analyst in order to create musically interesting results. [ABSTRACT FROM PUBLISHER]

This paper reports on data mining of Cretan folk songs for distinctive patterns. A pattern is distinctive if it occurs with higher probability in a corpus as compared to an anticorpus. A small set of transcribed Cretan folk songs was encoded, organized using a knowledge base of classes, and mined using distinctive pattern discovery methods. In this exploratory study several highly distinctive melodic patterns emerge, indicating the ability of distinctive patterns to describe subgroups of folk songs. [ABSTRACT FROM AUTHOR]

This special issue of the Journal of Mathematics and Music addresses the topic of computational music analysis. It arose from a series of two international workshops on the topic, one in Berlin and one in Paris, both of which created interesting discussions and debates. In the call for papers, this special issue welcomed previously unpublished contributions that presented computational approaches of any type of music analysis. As a special focus, all papers were asked to analyse the same piece: the first movement of Brahms' String Quartet No. 1. The aim was to bring together diverse computational analytical approaches and methodologies, such as structural, motivic, semiotic, comparative, reductional, harmonic, transformational, and others, using a variety of computational implementation techniques. By focusing on to the same piece, similarities, differences, and complementarities among the approaches on both the methodological and the analytical results levels could be more easily observed. Authors were particularly encouraged to consider Forte's [1] and Huron's [2] analyses of the string quartet, and relate them to their own work if possible. Three papers were chosen for publication, which reflect the various aspects and levels of computation involved. [ABSTRACT FROM AUTHOR]