Download Neural Grey-Box Guitar Amplifier Modelling with Limited Data This paper combines recurrent neural networks (RNNs) with the discretised Kirchhoff nodal analysis (DK-method) to create a grey-box guitar amplifier model. Both the objective and subjective results suggest that the proposed model is able to outperform a baseline black-box RNN model in the task of modelling a guitar amplifier, including realistically recreating the behaviour of the amplifier equaliser circuit, whilst requiring significantly less training data. Furthermore, we adapt the linear part of the DK-method in a deep learning scenario to derive multiple state-space filters simultaneously. We frequency sample the filter transfer functions in parallel and perform frequency domain filtering to considerably reduce the required training times compared to recursive state-space filtering. This study shows that it is a powerful idea to separately model the linear and nonlinear parts of a guitar amplifier using supervised learning.
Download Simulating guitar distortion circuits using wave digital and nonlinear state-space formulations This work extends previous research on numerical solution of nonlinear systems in musical acoustics to the realm of nonlinear musical circuits. Wave digital principles and nonlinear state-space simulators provide two alternative approaches explored in this work. These methods are used to simulate voltage amplification stages typically used in guitar distortion or amplifier circuits. Block level analysis of the entire circuit suggests a strategy based upon the nonlinear filter composition technique for connecting amplifier stages while accounting for the way these stages interact. Formulations are given for the bright switch, the diode clipper, a transistor amplifier, and a triode amplifier.
Download Sample Rate Independent Recurrent Neural Networks for Audio Effects Processing In recent years, machine learning approaches to modelling guitar amplifiers and effects pedals have been widely investigated and have become standard practice in some consumer products. In particular, recurrent neural networks (RNNs) are a popular choice for modelling non-linear devices such as vacuum tube amplifiers and distortion circuitry. One limitation of such models is that they are trained on audio at a specific sample rate and therefore give unreliable results when operating at another rate. Here, we investigate several methods of modifying RNN structures to make them approximately sample rate independent, with a focus on oversampling. In the case of integer oversampling, we demonstrate that a previously proposed delay-based approach provides high fidelity sample rate conversion whilst additionally reducing aliasing. For non-integer sample rate adjustment, we propose two novel methods and show that one of these, based on cubic Lagrange interpolation of a delay-line, provides a significant improvement over existing methods. To our knowledge, this work provides the first in-depth study into this problem.
Download Aliasing Reduction in Neural Amp Modeling by Smoothing Activations The increasing demand for high-quality digital emulations of analog audio hardware, such as vintage tube guitar amplifiers, led
to numerous works on neural network-based black-box modeling,
with deep learning architectures like WaveNet showing promising
results. However, a key limitation in all of these models was the
aliasing artifacts stemming from nonlinear activation functions in
neural networks. In this paper, we investigated novel and modified activation functions aimed at mitigating aliasing within neural
amplifier models. Supporting this, we introduced a novel metric,
the Aliasing-to-Signal Ratio (ASR), which quantitatively assesses
the level of aliasing with high accuracy. Measuring also the conventional Error-to-Signal Ratio (ESR), we conducted studies on a
range of preexisting and modern activation functions with varying
stretch factors. Our findings confirmed that activation functions
with smoother curves tend to achieve lower ASR values, indicating a noticeable reduction in aliasing. Notably, this improvement
in aliasing reduction was achievable without a substantial increase
in ESR, demonstrating the potential for high modeling accuracy
with reduced aliasing in neural amp models.
Download Improving Unsupervised Clean-to-Rendered Guitar Tone Transformation Using GANs and Integrated Unaligned Clean Data Recent years have seen increasing interest in applying deep learning methods to the modeling of guitar amplifiers or effect pedals. Existing methods are mainly based on the supervised approach, requiring temporally-aligned data pairs of unprocessed and rendered audio. However, this approach does not scale well, due to the complicated process involved in creating the data pairs. A very recent work done by Wright et al. has explored the potential of leveraging unpaired data for training, using a generative adversarial network (GAN)-based framework. This paper extends their work by using more advanced discriminators in the GAN, and using more unpaired data for training. Specifically, drawing inspiration from recent advancements in neural vocoders, we employ in our GANbased model for guitar amplifier modeling two sets of discriminators, one based on multi-scale discriminator (MSD) and the other multi-period discriminator (MPD). Moreover, we experiment with adding unprocessed audio signals that do not have the corresponding rendered audio of a target tone to the training data, to see how much the GAN model benefits from the unpaired data. Our experiments show that the proposed two extensions contribute to the modeling of both low-gain and high-gain guitar amplifiers.
Download Simulation of Fender Type Guitar Preamp using Approximation and State-Space Model This paper deals with usage of approximations for simulation of more complex audio circuits. A Fender type guitar preamp was chosen as a case study. This circuit contains two tubes and thus four nonlinear functions as well as it is a parametric circuit because of an integrated tone stack. A state-space approach was used for simulation and further, precomputed solution is approximated using nonuniform cubic splines.
Download Differentiable White-Box Virtual Analog Modeling Component-wise circuit modeling, also known as “white-box”
modeling, is a well established and much discussed technique in
virtual analog modeling. This approach is generally limited in accuracy by lack of access to the exact component values present in
a real example of the circuit. In this paper we show how this problem can be addressed by implementing the white-box model in a
differentiable form, and allowing approximate component values
to be learned from raw input–output audio measured from a real
device.
Download Block-oriented modeling of distortion audio effects using iterative minimization Virtual analog modeling is the process of digitally recreating an analog device. This study focuses on analog distortion pedals for guitarists, which are categorized as stompboxes, because the musician turns them on and off by stepping on the switch. While some of the current digital models of distortion effects are circuit-based, this study uses a signal-based approach to identify the device under test (DUT). An algorithm to identify any distortion effect pedal in any given setting by input-output (I/O) measurements is proposed. A parametric block-oriented Wiener-Hammerstein model for distortion effects and the corresponding iterative error minimization procedure are introduced. The algorithm is implemented in Matlab and uses the Levenberg-Marquardt minimization procedure with boundaries for the parameters.
Download Amp-Space: A Large-Scale Dataset for Fine-Grained Timbre Transformation We release Amp-Space, a large-scale dataset of paired audio
samples: a source audio signal, and an output signal, the result of
a timbre transformation. The types of transformations we study
are from blackbox musical tools (amplifiers, stompboxes, studio
effects) traditionally used to shape the sound of guitar, bass, or
synthesizer sounds. For each sample of transformed audio, the
set of parameters used to create it are given. Samples are from
both real and simulated devices, the latter allowing for orders of
magnitude greater data than found in comparable datasets. We
demonstrate potential use cases of this data by (a) pre-training a
conditional WaveNet model on synthetic data and show that it reduces the number of samples necessary to digitally reproduce a
real musical device, and (b) training a variational autoencoder to
shape a continuous space of timbre transformations for creating
new sounds through interpolation.
Download Deep Learning Conditioned Modeling of Optical Compression Deep learning models applied to raw audio are rapidly gaining relevance in modeling audio analog devices. This paper investigates the use of different deep architectures for modeling audio optical compression. The models use as input and produce as output raw audio samples at audio rate, and it works with noor small-input buffers allowing a theoretical real-time and lowlatency implementation. In this study, two compressor parameters, the ratio, and threshold have been included in the modeling process aiming to condition the inference of the trained network. Deep learning architectures are compared to model an all-tube optical mono compressor including feed-forward, recurrent, and encoder-decoder models. The results of this study show that feedforward and long short-term memory architectures present limitations in modeling the triggering phase of the compressor, performing well only on the sustained phase. On the other hand, encoderdecoder models outperform other architectures in replicating the overall compression process, but they overpredict the energy of high-frequency components.