Signal Complexification using Frequency Modulation and Neuroevolution

The automatic generation of dynamic or complex audio signals has a wide range of applications, from sound effects design to virtual instrument implementation. Techniques for the creation of content in these domains typically involve searching though large input spaces in order to find combinations that produce interesting results. These input spaces can be viewed as all the possible input signals, filter types, filter settings, etc., that might be used in order to generate a new signal. Our goal here is to automate this search process. This paper presents an audio generation system that combines simple FM Synthesis with a complexifiying neural network based on the NEAT system. Interesting results were found with our system that provide insight and motivation for future work on this topic.