Imagética na improvisação a duas vozes : um estudo reflexivo

Mestrado em MúsicaNeste trabalho, exploro a imagética e compreensão musicais na improvisação a duas vozes ao piano, numa perspectiva fenomenológica. Para isso, realizei um estudo auto-reflexivo em que descrevo a imagética musical, com especial enfoque na audiação, que observei durante as quatro fases do projecto: (1) estudo de intervalos, (2) estudo da Fuga em Lá maior de Mário Laginha, (3) composição e estudo de exercícios e (4) improvisação e a sequência de aprendizagem de conteúdo tonal de GordonIn this thesis, I explore musical imagery and comprehension in the context of two-part improvisation at the piano, from a phenomenological perspective. To accomplish that, I performed a self-reflexive study in which I describe the musical imagery, particularly audiation that I observed during the four phases of this project: (1) study of intervals, (2) study of Mário Laginha's Fugue in A major, (3) composition and practice of exercises and (4) improvisation and Gordon's tonal learning sequence

A Neural Network Model of Metric Perception and Cognition in the Audition of Functional Tonal Music.

In our previous work we proposed a theory of cognition of tonal music based on control of expectations and created a model to test the theory using a hierarchical sequential neural network. The net learns metered and rhythmecized functional tonal harmonic progressions allowing us to measure fluctuations in the degree of realized expectation (DRE). Preliminary results demonstrated the necessity of including metric information in the model in order to obtain more realistic results for the model of the DRE. This was achieved by adding two units representing periodic index of meter to the input layer. In this paper we describe significant extensions to the architecture. Specifically, our goal was to represent more general meter tracking strategies and consider their implications as cognitive models. The output layer of the sub-net for metric information is fully connected to the hidden layer of sequential net. This output layer includes pools of three and four units representing duple and ..

A Neural Network Model of Metric Perception and Cognition in the Audition of Functional Tonal Music.

We describe an extension of our model of music cognition based on fluctuations in the degree of realized expectation (DRE) ([Gang and Berger, 1996] and [Berger and Dang, 1996]). In our previous work we proposed a theory of cognition of tonal music based on control of expectations and created a model to test the theory using a hierarchical sequential neural network. The hierarchical sequential net learns metered and rhythmecized functional tonal harmonic progressions allowing us to measure fluctuations in the degree of realized expectation (DRE). The three layered sequential net includes twelve state units (the chromatic scale) and twelve output units represent harmonic events as pitch class combinations. Preliminary results demonstrated the necessity of including metric information in the model in order to obtain more realistic results for the model of the DRE. This was achieved by adding tw