| Abstract: |  While they strive to free themselves from musical conventions, many contemporary composers still regard music as a ’language’. However, can innovation be reconciled with communication? Music seems to convey emotions largely through choreographing listener expectancies. The well-known strong expectancy of leading tone resolving to tonic (the ’attraction’ of the former to the latter) can be simulated computationally with a gradient-frequency-neural- network-based artificial neural network (GFNN-ANN). Moreover, GFNN-ANNs seem to have the potential to simulate attractions pertaining to any musical sound, thus providing composer with a code for communicating emotions through novel sounds. Consequently, I have implemented a GFNN-ANN, tuned its parameters manually, and tested experimentally how well it fulfills the potential. Although the results were not particularly satisfactory, GFNN-ANNs are, in my opinion, worth exploring further. However, a faster implementation will be needed.
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