Modeling of the acoustic signal of an electric guitar amplifier using recurrent neural networks

Neural networks have topped performance measures across a wide variety of computational tasks. These performances are prevalent within the domain of human perception type tasks such as classification or generation of images, audio, or text. Recurrent neural networks are neural network architecture of choice for time-domain data such as spoken or written human language. Recurrent neural networks have also shown promise for tasks in the specialized signal domain of music. This thesis explores using recurrent neural networks to model the particular musical qualities generated by analog electronic musical equipment. The electric guitar amplifier is used by electric guitar players to shape the timbre of their musical instrument as they play. Most professionals consider analog amplifiers designed to provide acoustic distortion with vacuum tubes as having the best sound and feel for musicians. We attempted to model the sound transformation of a vacuum tube based electric guitar amplifier using a convolutional recurrent neural network architecture. For our experiment, we trained recurrent neural networks of various architectures using inputs of electric guitar signals and the subsequent signal processed through a typical vacuum-tube based amplifier and audio recording equipment. Training data was collected according to the recommended specifications of previous experiments. The amp simulation models were compared against the original amplifier with signal analysis and subjective listening tests. Sound recording techniques for capturing the best input and output data for the current state-of-the-art were analyzed. Analysis of various model input configurations and hyperparameter settings also showed several major limitations in the ability of the model to accurately reproduce the acoustic properties of a signal chain with more complex distortion characteristics than those previously tested. The limitations were analyzed and features of a new model were proposed. Subjective listening tests with three expert musical listeners showed that even though listeners could identify the quality degradation of the model, some listeners found the signals provided by specific models to be of more interest to their musical tastes. These models tended to model an amplifier set to a smooth gentle tone, which the model made less harmonically rich. However, models trained on amplifier tones with dense harmonic distortion characteristics were uniformly judged as very poor quality. These models could not accurately reproduce the intense compression and non-linear distortion qualities and ended up sounding abrupt with a hissing buzz.