Paper Archive

Contemporary musicians commonly face the challenge of finding new, characteristic sounds that can make their compositions more distinct. They often resort to computers and algorithms, which can significantly aid in creative processes by generating unexpected material in controlled probabilistic processes. In particular, algorithms that present emergent behaviors, like genetic algorithms and cellular automata, have fostered a broad diversity of musical explorations. This article proposes an original technique for the computer-assisted creation and manipulation of sound textures. The technique uses Probabilistic Cellular Automata, which are yet seldom explored in the music domain, to blend two audio tracks into a third, different one. The proposed blending process works by dividing the source tracks into frequency bands and then associating each of the automaton’s cell to a frequency band. Only one source, chosen by the cell’s state, is active within each band. The resulting track has a non-repeating textural pattern that follows the changes in the Cellular Automata. This blending process allows the musician to choose the original material and the blend granularity, significantly changing the resulting blends. We demonstrate how to use the proposed blending process in sound design and its application in experimental and popular music.

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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.

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For physical modelling sound synthesis, many techniques are available; time-stepping methods (e.g., finite-difference time-domain (FDTD) methods) have an advantage of flexibility and generality in terms of the type of systems they can model. These methods do, however, lack the capability of easily handling smooth parameter changes while retaining optimal simulation quality and stability, something other techniques are better suited for. In this paper, we propose an efficient method to smoothly add and remove grid points from a FDTD simulation under sub-audio rate parameter variations. This allows for dynamic parameter changes in physical models of musical instruments. An instrument such as the trombone can now be modelled using FDTD methods, as well as physically impossible instruments where parameters such as e.g. material density or its geometry can be made time-varying. Results show that the method does not produce (visible) artifacts and stability analysis is ongoing.

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In recent years, a range of topological methods have emerged for processing digital signals. In this paper we show how the construction of topological filters via sheaves can be used to topologize existing sound synthesis methods. I illustrate this process on two classes of synthesis approaches: (1) based on linear-time invariant digital filters and (2) based on oscillators defined on a circle. We use the computationally-friendly approach to modeling topologies via a simplicial complex, and we attach our classical synthesis methods to them via sheaves. In particular, we explore examples of simplicial topologies that mimic sampled lines and loops. Over these spaces we realize concrete examples of simple discrete harmonic oscillators (resonant filters), and simple comb filter based algorithms (such as Karplus-Strong) as well as frequency modulation.

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While immersive music productions have become popular in recent years, music content produced during the last decades has been predominantly mixed for stereo. This paper presents a datadriven approach to automatic binaural upmixing of stereo music. The network architecture HDemucs, previously utilized for both source separation and binauralization, is leveraged for an endto-end approach to binaural upmixing. We employ two distinct datasets, demonstrating that while custom-designed training data enhances the accuracy of spatial positioning, the use of professionally mixed music yields superior spatialization. The trained networks show a capacity to process multiple simultaneous sources individually and add valid binaural cues, effectively positioning sources with an average azimuthal error of less than 11.3 ◦ . A listening test with binaural experts shows it outperforms digital signal processing-based approaches to binauralization of stereo content in terms of spaciousness while preserving audio quality.

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Nonlinear digital circuits and waveshaping are active areas of study, specifically for what concerns numerical and aliasing issues. In the past, an effective method was proposed to discretize nonlinear static functions with reduced aliasing based on the antiderivative of the nonlinear function. Such a method is based on the continuoustime convolution with an FIR antialiasing filter kernel, such as a rectangular kernel. These kernels, however, are far from optimal for the reduction of aliasing. In this paper we introduce the use of arbitrary IIR rational transfer functions that allow a closer approximation of the ideal antialiasing filter, required in the fictitious continuous-time domain before sampling the nonlinear function output. These allow a higher degree of aliasing reduction and can be flexibly adjusted to balance performance and computational cost.

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The recently proposed antiderivative antialiasing (ADAA) technique for stateful systems involves two key features: 1) replacing a nonlinearity in a physical model or virtual analog simulation with an antialiased nonlinear system involving antiderivatives of the nonlinearity and time delays and 2) introducing a digital filter in cascade with each original delay in the system. Both of these features introduce the same delay, which is compensated by adjusting the sampling period. The result is a simulation with reduced aliasing distortion. In this paper, we study ADAA using equivalent circuits, answering the question: “Which electrical circuit, discretized using the bilinear transform, yields the ADAA system?” This gives us a new way of looking at the stability of ADAA and how introducing extra filtering distorts a system’s response. We focus on the Wave Digital Filter (WDF) version of this technique.

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Estimating mixtures of damped chirp sinusoids in noise is a problem that affects audio analysis, coding, and synthesis applications. Phase-based non-stationary parameter estimators assume that sinusoids can be resolved in the Fourier transform domain, whereas high-resolution methods estimate superimposed components with accuracy close to the theoretical limits, but only for sinusoids with constant frequencies. We present a new method for estimating the parameters of superimposed damped chirps that has an accuracy competitive with existing non-stationary estimators but also has a high-resolution like subspace techniques. After providing the analytical expression for a Gaussian-windowed damped chirp signal’s Fourier transform, we propose an efficient variational EM algorithm for nonlinear Bayesian regression that jointly estimates the amplitudes, phases, frequencies, chirp rates, and decay rates of multiple non-stationary components that may be obfuscated under the same local maximum in the frequency spectrum. Quantitative results show that the new method not only has an estimation accuracy that is close to the Cramér-Rao bound, but also a high resolution that outperforms the state-of-the-art.

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We propose a new analog filter discretization method that is useful for discretizing systems with features near or above the Nyquist limit. A conformal mapping approach is taken, and we introduce the peaking conformal map and shelving conformal map. The proposed method provides a close match to the original analog frequency response below half the sampling rate and is parameterizable, order preserving, and agnostic to the original filter’s order or type. The proposed method should have applications to discretizing filters that have time-varying parameters or need to be implemented across many different sampling rates.

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Sustained contact interactions like scraping and rolling produce a wide variety of sounds. Previous studies have explored ways to synthesize these sounds efficiently and intuitively but could not fully mimic the rich structure of real instances of these sounds. We present a novel source-filter model for realistic synthesis of scraping and rolling sounds with physically and perceptually relevant controllable parameters constrained by principles of mechanics. Key features of our model include non-linearities to constrain the contact force, naturalistic normal force variation for different motions, and a method for morphing impulse responses within a material to achieve location-dependence. Perceptual experiments show that the presented model is able to synthesize realistic scraping and rolling sounds while conveying physical information similar to that in recorded sounds.

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