Style Transfer for Non-differentiable Audio Effects
This addresses the difficulty for audio engineers in manually tuning complex audio effect parameters, though it is incremental as it builds on existing style transfer methods by extending them to non-differentiable effects.
The paper tackled the problem of learning parameter configurations for non-differentiable audio effects in style transfer, proposing a deep learning approach that works with widely used frameworks and showing it can convincingly style match a multi-band compressor effect in listening tests.
Digital audio effects are widely used by audio engineers to alter the acoustic and temporal qualities of audio data. However, these effects can have a large number of parameters which can make them difficult to learn for beginners and hamper creativity for professionals. Recently, there have been a number of efforts to employ progress in deep learning to acquire the low-level parameter configurations of audio effects by minimising an objective function between an input and reference track, commonly referred to as style transfer. However, current approaches use inflexible black-box techniques or require that the effects under consideration are implemented in an auto-differentiation framework. In this work, we propose a deep learning approach to audio production style matching which can be used with effects implemented in some of the most widely used frameworks, requiring only that the parameters under consideration have a continuous domain. Further, our method includes style matching for various classes of effects, many of which are difficult or impossible to be approximated closely using differentiable functions. We show that our audio embedding approach creates logical encodings of timbral information, which can be used for a number of downstream tasks. Further, we perform a listening test which demonstrates that our approach is able to convincingly style match a multi-band compressor effect.