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 Antialiased Black-Box Modeling of Audio Distortion Circuits Using Real Linear Recurrent Units In this paper, we propose the use of real-valued Linear Recurrent
Units (LRUs) for black-box modeling of audio circuits. A network architecture composed of real LRU blocks interleaved with
nonlinear processing stages is proposed.
Two case studies are
presented, a second-order diode clipper and an overdrive distortion pedal. Furthermore, we show how to integrate the antiderivative antialiaisng technique into the proposed method, effectively
lowering oversampling requirements. Our experiments show that
the proposed method generates models that accurately capture the
nonlinear dynamics of the examined devices and are highly efficient, which makes them suitable for real-time operation inside
Digital Audio Workstations.
Download Differentiable IIR Filters for Machine Learning Applications In this paper we present an approach to using traditional digital IIR
filter structures inside deep-learning networks trained using backpropagation. We establish the link between such structures and
recurrent neural networks. Three different differentiable IIR filter
topologies are presented and compared against each other and an
established baseline. Additionally, a simple Wiener-Hammerstein
model using differentiable IIRs as its filtering component is presented and trained on a guitar signal played through a Boss DS-1
guitar pedal.
Download Modelling of nonlinear state-space systems using a deep neural network In this paper we present a new method for the pseudo black-box modelling of general continuous-time state-space systems using a discrete-time state-space system with an embedded deep neural network. Examples are given of how this method can be applied to a number of common nonlinear electronic circuits used in music technology, namely two kinds of diode-based guitar distortion circuits and the lowpass filter of the Korg MS-20 synthesizer.
Download Time-Variant Gray-Box Modeling of a Phaser Pedal A method to measure the response of a linear time-variant (LTV) audio system is presented. The proposed method uses a series of short chirps generated as the impulse response of several cascaded allpass filters. This test signal can measure the characteristics of an LTV system as a function of time. Results obtained from testing of this method on a guitar phaser pedal are presented. A proof of concept gray-box model of the measured system is produced based on partial knowledge about the internal structure of the pedal and on the spectral analysis of the measured responses. The temporal behavior of the digital model is shown to be very similar to that of the measured device. This demonstrates that it is possible to measure LTV analog audio systems and produce approximate virtual analog models based on these results.
Download Rounding Corners with BLAMP The use of the bandlimited ramp (BLAMP) function as an antialiasing tool for audio signals with sharp corners is presented. Discontinuities in the waveform of a signal or its derivatives require infinite bandwidth and are major sources of aliasing in the digital domain. A polynomial correction function is modeled after the ideal BLAMP function. This correction function can be used to treat aliasing caused by sharp edges or corners which translate into discontinuities in the first derivative of a signal. Four examples of cases where these discontinuities appear are discussed: synthesis of triangular waveforms, hard clipping, and half-wave and fullwave rectification. Results obtained show that the BLAMP function is a more efficient tool for alias reduction than oversampling. The polynomial BLAMP can reduce the level of aliasing components by up to 50 dB and improve the overall signal-to-noise ratio by about 20 dB. The proposed method can be incorporated into virtual analog models of musical systems.