Deep Audio Effects for Snare Drum Recording Transformations

Abstract

The ability to perceptually modify drum recording parameters in a post-recording process would be of great benefit to engineers limited by time or equipment. In this work, a datadriven approach to post-recording modification of the dampening and microphone positioning parameters commonly associated with snare drum capture is proposed. The system consists of a deep encoder that analyzes audio input and predicts optimal parameters of one or more third-party audio effects, which are then used to process the audio and produce the desired transformed output audio. Furthermore, two novel audio effects are specifically developed to take advantage of the multiple parameter learning abilities of the system. Perceptual quality of transformations is assessed through a subjective listening test, and an object evaluation is used to measure system performance. Results demonstrate a capacity to emulate snare dampening; however, attempts were not successful for emulating microphone position changes.