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 uctuations 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 signi cant extensions to the architecture. Speci cally, 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 triple metric indices. Thus the sub-net was able to in uence the resulting DRE, that was expected by the net. Moreover, by including multiple metric parsings in the output layer the net re ects con icts between parallel possible interpretations of meter. This output was fed back into the sub-net to in uence the next predictions of the DRE and the meter. In addition, the target harmony element was fed into the context instead of the actual output, thus simulating the interactive in uences of harmonic rhythm and meter.