Download Anti-Aliasing of Neural Distortion Effects via Model Fine Tuning Neural networks have become ubiquitous with guitar distortion
effects modelling in recent years. Despite their ability to yield
perceptually convincing models, they are susceptible to frequency
aliasing when driven by high frequency and high gain inputs.
Nonlinear activation functions create both the desired harmonic
distortion and unwanted aliasing distortion as the bandwidth of
the signal is expanded beyond the Nyquist frequency. Here, we
present a method for reducing aliasing in neural models via a
teacher-student fine tuning approach, where the teacher is a pretrained model with its weights frozen, and the student is a copy of
this with learnable parameters. The student is fine-tuned against
an aliasing-free dataset generated by passing sinusoids through
the original model and removing non-harmonic components from
the output spectra.
Our results show that this method significantly suppresses aliasing for both long-short-term-memory networks (LSTM) and temporal convolutional networks (TCN). In the
majority of our case studies, the reduction in aliasing was greater
than that achieved by two times oversampling. One side-effect
of the proposed method is that harmonic distortion components
are also affected.
This adverse effect was found to be modeldependent, with the LSTM models giving the best balance between
anti-aliasing and preserving the perceived similarity to an analog
reference device.
Download Learning Nonlinear Dynamics in Physical Modelling Synthesis Using Neural Ordinary Differential Equations Modal synthesis methods are a long-standing approach for modelling distributed musical systems. In some cases extensions are
possible in order to handle geometric nonlinearities. One such
case is the high-amplitude vibration of a string, where geometric nonlinear effects lead to perceptually important effects including pitch glides and a dependence of brightness on striking amplitude. A modal decomposition leads to a coupled nonlinear system of ordinary differential equations. Recent work in applied machine learning approaches (in particular neural ordinary differential equations) has been used to model lumped dynamic systems
such as electronic circuits automatically from data. In this work,
we examine how modal decomposition can be combined with neural ordinary differential equations for modelling distributed musical systems. The proposed model leverages the analytical solution
for linear vibration of system’s modes and employs a neural network to account for nonlinear dynamic behaviour. Physical parameters of a system remain easily accessible after the training without
the need for a parameter encoder in the network architecture. As
an initial proof of concept, we generate synthetic data for a nonlinear transverse string and show that the model can be trained to
reproduce the nonlinear dynamics of the system. Sound examples
are presented.
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.