Tuning a Neural Network for Harmonizing Melodies in Real-Time

We describe a sequential neural network for harmonizing melodies in real-time. The network models aspects of human cognition and can be used as the basis for building an interactive system that automatically generates accompaniment for simple melodies in live performance situations. The net learns relations between important notes of the melody and their harmonies and is able to produce harmonies for new melodies in real-time, that is, without advanced knowledge of the continuation of the melody. We tackle the challenge of evaluating these harmonies by applying distance functions to measure the disparity between the net's choice of a chord and that of the author of the source book from which the melody was taken. We experimented with three major issues that have implications on the performance of the model: searching for the best learning parameters (e.g., the decay parameters), the size of the learning set and the influence of metric information. The decay parameters set the scope of ..