Kate Stevens, University of Western Sydney
Sue Becker and Laurel Trainor, McMaster University
We hypothesized that an artificial neural network model of melody cognition will exhibit signs of the IR principles during the course of learning and/or whilst performing a melody prediction task. Specifically, if the network is a valid model of melody cognition then, after exposure to examples of Western tonal melodies, the network will construct a set of connection strengths and hidden unit activations that permit: a) prediction of the next note in familiar melodies; and b) prediction of the note following an implicative interval that conform with Narmour's principles. Two feed-forward networks were constructed and exposed to identical sets of training and test patterns. The first network was designed to encourage the construction of an interval code as a result of exposure to examples of Western tonal melodies. An interval code is one that uses the pitch distance between each two successive tones and ignores the actual pitch values. With such a code and the computational power afforded by inclusion of two layers of hidden units, it was expected that performance of the multi-layer back-propagation network would surpass that of a single layer network as measured by melody prediction and tests on musical intervals.
The two models provide an existence proof that principles central to Narmour's model of bottom-up melodic expectancy can be learned by exposure to a set of Western tonal melodies. Although these relatively simple networks performed well, design modifications are needed to enable inclusion of duration and amplitude features. The existence of exceptions or irregular occurrences in music provides the motivation to model expectancy using adaptive mixtures of local experts (Jacobs, Jordan, Nowlan & Hinton, 1991). It would also be possible to build proximal pitch relations into a network by coarse coding the input pitch units and to measure the degree to which melody and interval prediction improves. Subsequent models that use networks to examine the way in which pitch, interval, scale step and key are acquired from the auditory environment and represented in memory will also be discussed.