A Neural Network Model of Metric Perception and Cognition in the Audition of Functional Tonal Music
Jonathan C Berger, Dan Gang · 1997
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 ...