Download Digital Audio Device Creation by the use of a Domain Specific Language and a Hardware Abstraction Layer The present paper deals with a framework destined to manage different aspects of the creation of digital audio devices. By means of a domain-specific language modelling aspects like signal processing and user interaction are implemented. The problem of different hardware interfaces is resolved by the definition of a hardware abstraction layer. This layer provides different types of variables and functions. A compiler translates the model referring the functions and variables defined at the hardware abstraction layer. Furthermore, the compiler is able to split the model into different parts that can be run on different hardware components. The communication needed to manage the distributed model is defined and formalized by the framework. A simple example is presented to help explain the framework’s parts, as are the compiler and the execution unit.
Download Antiderivative Antialiasing for Stateful Systems Nonlinear systems, like e.g. guitar distortion effects, play an important role in musical signal processing. One major problem encountered in digital nonlinear systems is aliasing distortion. Consequently, various aliasing reduction methods have been proposed in the literature. One of these is based on using the antiderivative of the nonlinearity and has proven effective, but is limited to memoryless systems. In this work, it is extended to a class of stateful systems which includes but is not limited to systems with a single one-port nonlinearity. Two examples from the realm of virtual analog modeling show its applicability to and effectiveness for commonly encountered guitar distortion effect circuits.
Download Real-Time Physical Modelling For Analog Tape Machines For decades, analog magnetic tape recording was the most popular method for recording music, but has been replaced over the past 30 years first by DAT tape, then by DAWs and audio interfaces. Despite being replaced by higher quality technology, many have sought to recreate a "tape" sound through digital effects, despite the distortion, tape "hiss", and other oddities analog tape produced. The following paper describes the general process of creating a physical model of an analog tape machine starting from basic physical principles, then discusses in-depth a real-time implementation of a physical model of a Sony TC-260 tape machine."Whatever you now find weird, ugly, uncomfortable, and nasty about a new medium will surely become its signature. CD distortion, the jitteriness of digital video, the crap sound of 8-bit - all of these will be cherished and emulated as soon as they can be avoided." -Brian Eno.
Download Continuous State Modeling for Statistical Spectral Synthesis Continuous State Markovian Spectral Modeling is a novel approach for parametric synthesis of spectral modeling parameters, based on the sines plus noise paradigm. The method aims specifically at capturing shimmer and jitter - micro-fluctuations in the partials’ frequency and amplitude trajectories, which are essential for the timbre of musical instruments. It allows for parametric control over the timbral qualities, while removing the need for the more computationally expensive and restrictive process of the discrete state space modeling method. A qualitative comparison between an original violin sound and a re-synthesis shows the ability of the algorithm to reproduce the micro-fluctuations, considering their stochastic and spectral properties.
Download GRAFX: An Open-Source Library for Audio Processing Graphs in Pytorch We present GRAFX, an open-source library designed for handling audio processing graphs in PyTorch. Along with various library functionalities, we describe technical details on the efficient parallel computation of input graphs, signals, and processor parameters in GPU. Then, we show its example use under a music mixing scenario, where parameters of every differentiable processor in a large graph are optimized via gradient descent. The code is available at https://github.com/sh-lee97/grafx.
Download SCHAEFFER: A Dataset of Human-Annotated Sound Objects for Machine Learning Applications Machine learning for sound generation is rapidly expanding within
the computer music community. However, most datasets used to
train models are built from field recordings, foley sounds, instrumental notes, or commercial music. This presents a significant
limitation for composers working in acousmatic and electroacoustic music, who require datasets tailored to their creative processes.
To address this gap, we introduce the SCHAEFFER Dataset (Spectromorphological Corpus of Human-annotated Audio with Electroacoustic Features For Experimental Research), a curated collection of 1000 sound objects designed and annotated by composers and students of electroacoustic composition. The dataset,
distributed under Creative Commons licenses, features annotations
combining technical and poetic descriptions, alongside classifications based on pre-defined spectromorphological categories.
Download Physical Modeling of the MXR Phase 90 Guitar Effect Pedal In this study, a famous boxed effect pedal, also called stompbox, for electrical guitars is analyzed and simulated. The nodal DK method is used to create a non-linear state-space system with Matlab as a physical model for the MXR Phase 90 guitar effect pedal. A crucial component of the effect are Junction Field Effect Transistors (JFETs) which are used as variable resistors to dynamically vary the phase-shift characteristic of an allpass-filter cascade. So far, virtual analog modeling in the context of audio has mainly been applied to diode-clippers and vacuum tube circuits. This work shows an efficient way of describing the nonlinear behavior of JFETs, which are wide-spread in audio devices. To demonstrate the applicability of the proposed physical model, a real-time VST audio plug-in was implemented.
Download A Complex Envelope Sinusoidal Model for Audio Coding A modification to the hybrid sinusoidal model is proposed for the purpose of high-quality audio coding. In our proposal the amplitude envelope of each harmonic partial is modeled by a narrowband complex signal. Such representation incorporates most of the signal energy associated with sinusoidal components, including that related to frequency estimation and quantization errors. It also takes into account the natural width of each spectral line. The advantages of such model extension are a more straightforward and robust representation of the deterministic component and a clean stochastic residual without ghost sinusoids. The reconstructed signal is virtually free from harmonic artifacts and more natural sounding. We propose to encode the complex envelopes by the means of MCLT transform coefficients with coefficient interleave across partials within an MPEG-like coding scheme. We show some experimental results with high compression efficiency achieved.
Download Sound texture synthesis using Convolutional Neural Networks The following article introduces a new parametric synthesis algorithm for sound textures inspired by existing methods used for visual textures. Using a 2D Convolutional Neural Network (CNN), a sound signal is modified until the temporal cross-correlations of the feature maps of its log-spectrogram resemble those of a target texture. We show that the resulting synthesized sound signal is both different from the original and of high quality, while being able to reproduce singular events appearing in the original. This process is performed in the time domain, discarding the harmful phase recovery step which usually concludes synthesis performed in the time-frequency domain. It is also straightforward and flexible, as it does not require any fine tuning between several losses when synthesizing diverse sound textures. Synthesized spectrograms and sound signals are showcased, and a way of extending the synthesis in order to produce a sound of any length is also presented. We also discuss the choice of CNN, border effects in our synthesized signals and possible ways of modifying the algorithm in order to improve its current long computation time.
Download Towards Efficient Emulation of Nonlinear Analog Circuits for Audio Using Constraint Stabilization and Convex Quadratic Programming This paper introduces a computationally efficient method for
the emulation of nonlinear analog audio circuits by combining state-space representations, constraint stabilization, and convex quadratic programming (QP). Unlike traditional virtual analog (VA) modeling approaches or computationally demanding
SPICE-based simulations, our approach reformulates the nonlinear
differential-algebraic (DAE) systems that arise from analog circuit
analysis into numerically stable optimization problems. The proposed method efficiently addresses the numerical challenges posed
by nonlinear algebraic constraints via constraint stabilization techniques, significantly enhancing robustness and stability, suitable
for real-time simulations. A canonical diode clipper circuit is presented as a test case, demonstrating that our method achieves accurate and faster emulations compared to conventional state-space
methods. Furthermore, our method performs very well even at
substantially lower sampling rates. Preliminary numerical experiments confirm that the proposed approach offers improved numerical stability and real-time feasibility, positioning it as a practical
solution for high-fidelity audio applications.