This project represents an important first step in determining the fundamental aesthetically relevant features of sound. Though there has been much effort in revealing the features learned by a deep neural network (DNN) trained on visual data, little effort in applying these techniques to a network trained on audio data has been performed. Importantly, these efforts in the audio domain often impose strong biases about relevant features (e.g., musical structure). In this project, a DNN is trained to mimic the acoustic aesthetic judgment of a professional composer. A unique corpus of sounds and corresponding professional aesthetic judgments is leveraged for this purpose. By applying a variation of Google's "DeepDream" algorithm to this trained DNN, and limiting the assumptions introduced, we can begin to listen to and examine the features of sound fundamental for aesthetic judgment.