Download Exposure Bias and State Matching in Recurrent Neural Network Virtual Analog Models Virtual analog (VA) modeling using neural networks (NNs) has
great potential for rapidly producing high-fidelity models. Recurrent neural networks (RNNs) are especially appealing for VA due
to their connection with discrete nodal analysis. Furthermore, VA
models based on NNs can be trained efficiently by directly exposing them to the circuit states in a gray-box fashion. However,
exposure to ground truth information during training can leave the
models susceptible to error accumulation in a free-running mode,
also known as “exposure bias” in machine learning literature. This
paper presents a unified framework for treating the previously
proposed state trajectory network (STN) and gated recurrent unit
(GRU) networks as special cases of discrete nodal analysis. We
propose a novel circuit state-matching mechanism for the GRU
and experimentally compare the previously mentioned networks
for their performance in state matching, during training, and in exposure bias, during inference. Experimental results from modeling
a diode clipper show that all the tested models exhibit some exposure bias, which can be mitigated by truncated backpropagation
through time. Furthermore, the proposed state matching mechanism improves the GRU modeling performance of an overdrive
pedal and a phaser pedal, especially in the presence of external
modulation, apparent in a phaser circuit.
Download Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.