Paper Archive

In electric guitars, the vibrations of the strings are typically sensed by coils of wire combined with a magnet, called pickups. The pickups and their position along the strings contribute strongly to the instrument's sound. Most guitars feature one to three pickups, each spanning across all strings with fixed positions and generating a single mono output. The work of this Master's Thesis at ETH Zürich introduces a new pickup system called PolyMap, which senses each string individually and at multiple locations. The system is demonstrated with a custom-made eight-string guitar that contains eight pickups per string for a total of 64 pickups. The signals from these 64 pickups are individually digitized inside the guitar and transmitted over a multichannel audio digital interface (MADI), a low-latency digital audio interface, to a computer for further processing. PolyMap enables high-resolution sensing of an electric guitar's strings and enables extensive post-processing capabilities for musicians, audio engineers, and researchers. To the best of our knowledge, this is the first polyphonic guitar pickup system with such a complete feature set.

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WaveNet-style convolutional networks emulate tube amplifiers and distortion pedals with high fidelity, but their computational cost has confined them to desktops or dedicated DSP hardware. We present a sparse-enabled WaveNet inference engine for iOS that runs heavily pruned neural guitar amplifier models in real time on iPhones. Aggressive iterative magnitude pruning removes 90% of the network weights with no perceptible loss in quality. A custom sparse C++ engine turns this sparsity directly into compute savings, sustaining low-latency real-time operation on a CPU-only iPhone implementation where the dense model cannot. On-device output matches the trained model to within int16 quantization error. At the demonstration, visitors will play a guitar through the app on iPhone hardware and A/B the on-device pruned model against the physical pedal it emulates. Source code and audio examples are available online.

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This demonstration presents Praat AudioTools, an open-source hybrid toolkit that repurposes Praat's phonetic-analysis environment for electroacoustic composition, sound design, and offline analysis–resynthesis workflows. Rather than treating analysis data as temporary measurements hidden inside an audio processor, Praat AudioTools exposes pitch contours, formant structures, temporal segmentations, spectral descriptors, phrase boundaries, stochastic trajectories, and host-application exchange files as editable compositional objects. These objects can be inspected, modified, chained, reused, and rendered into new sound transformations. The demonstration focuses on seven offline workflows: Neural Ambient Drone Designer, Praat for Max and Max for Live, Phase-Space Composer, Reich Generator, MCMC Musical Variation, Messagesquisse Opening, and Vector/Full-Chain composition workflows. None of the examples are presented as real-time effects. Instead, they show an "edit-in-the-middle" model in which sound is analyzed, intermediate representations are made visible, compositional decisions are applied to those representations, and the result is rendered as audio. The aim is to demonstrate a transparent alternative to both conventional black-box audio effects and end-to-end generative audio systems: a compositional environment where analysis objects become controllers, traces, scores, and reproducible technical artifacts.

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This work extends our previously proposed Quadratic Programming (QP) approach for the emulation of nonlinear analog audio circuits by formalizing its main numerical ingredients and introducing a residual-driven adaptive multi-rate scheme. Starting from a state-space Differential Algebraic System of Equations (DAE) formulation, the nonlinear algebraic circuit device relations are replaced inside the QP by a first-order surrogate linear constraint, and the post-step nonlinear residual is shown to act as a valid defect indicator for adaptive step-size control. This yields a single-step simulation procedure that avoids the usual combination of nonlinear iterative solves and separate integration updates. The method is evaluated on a diode clipper, a BJT common-emitter amplifier, and a Colpitts oscillator, using SPICE as a baseline reference. The results show that adaptive step sizing considerably improves agreement with the reference solution, that the pseudo-inverse implementation is essentially equivalent to the full equality-constrained QP in the tested cases, and that the proposed formulation remains effective beyond the baseline clipper example, including for a self-oscillating circuit. These results position the proposed method as a promising bridge between SPICE-like interpretability and the efficiency demands of virtual analog (VA) audio applications.

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This work examines the use of the time-varying delay line (TVDL) to implement loopback frequency modulation (LBFM), an oscillator that loops back to modulate its own frequency. Digital delay lines are used regularly in sound synthesis/processing to model the pure delay associated with one-dimensional acoustic propagation. When the delay is made time varying, the TVDL time warps the input according to a delay function, altering the input's instantaneous frequency and phase. As a result, the TVDL is well suited for delay-based effects and, in particular, those involving frequency/phase modulation for which the TVDL delay function is oscillatory and thus bounded by a maximum and minimum delay. Sustaining a constant change in sounding frequency however, corresponds to a delay function having a term that is linear in time, making it limited only by the length of the input signal. While TVDLs may still be used when there is a pitch shift, limiting the delay by simple wrapping of the delay function and/or cross fading between multiple TVDLs may not be adequate to avoid audible artifacts. In LBFM, the resulting phase has both linear and oscillating terms and the resulting signal undergoes a sustained shift in the fundamental frequency that makes a TVDL implementation more challenging. An alternate closed-form representation of the LBFM oscillator, however, provides the information necessary for accurately wrapping the TVDL delay function and ensuring it is suitably bounded so that the produced sound is free of phase distortion and audible artifacts.

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Given a target sound, finding the synthesizer preset that best reproduces it remains a core problem in sound design. Existing methods treat synthesis parameters as flat vectors, discarding the signal routing and parameter interactions that produce audio. We make two contributions. First, to learn a representation of parameters including their signal routing, we design a graph neural network whose message passing structure imitates FM signal processing. Second, we adapt the multimodal objective from SLAP to learn joint embeddings of audio and FM synthesizer parameters, enabling preset retrieval from a gallery. We focus on the Yamaha DX7, where six identical sinusoid operators interact according to one of 32 routing topologies. Our graph encoder's message passing weights are shared across all nodes and layers, enabling processing of arbitrary topologies of any size. When every topology is seen during training, the DX7-GNN and two baselines achieve strong audio-to-preset retrieval. When some topologies are held out for testing, the DX7-GNN substantially outperforms both baselines despite having the fewest parameters. Our ablations further support the claim that imitating FM signal flow in a parameter encoder improves generalization to unseen topologies.

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We present a self-supervised, event-based sound matching model using a differentiable extended Karplus-Strong algorithm. To avoid relying on external onset and fundamental frequency detectors, we explore training methodologies combining parameter losses on synthetic data with audio losses. We demonstrate that time-domain fractional delay interpolation provides gradient accuracy comparable to frequency-sampling while avoiding time-aliasing in highly resonant time-varying scenarios. Through systematic gradient analysis, we reveal that standard spectral losses provide no meaningful directional gradients for onset times, heavily degrading joint training. Training exclusively with parameter losses on synthetic data effectively learns fundamental frequency, timbral parameters, and onset times, but struggles to generalise to monophonic studio recordings of plucked guitar. External detectors combined with audio losses generalise best, isolating the model to timbre optimisation. While our Karplus-Strong decoder recovers interpretable parameters and naturally captures the transient characteristics of plucked guitar, Harmonics plus Noise baselines yield higher reconstruction fidelity by most metrics.

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Polygonal synthesis generates audio by traversing the perimeter of a polygon with a phasor; prior work uses a constant angular velocity, whereas the proposed system adopts constant arc-length (perimeter) velocity. Existing formulations operate on regular, parametrically defined polygons, producing smooth timbral transitions within a single family of shapes. This paper generalizes polygonal synthesis around a unified arc-length engine: vertex data of any origin feed the same DSP pipeline. First, we adapt the oscillator to accept arbitrary vertex configurations from an external buffer, opening the possibility for a broad class of closed polygons — regular, irregular, or star-shaped — to function as a waveform generator. Second, a hybrid interpolation algorithm enables smooth morphing between polygons with unequal vertex counts, passing through intermediate shapes that have no parametric description. Third, we extend the paradigm to three dimensions: a convex polyhedron rotated about three axes is sliced by a fixed horizontal plane, and the resulting cross-section yields a continuously variable polygon controlled by the solid's orientation. The system runs in RNBO (Cycling '74) with a geometry caching strategy that avoids per-sample recomputation. Antialiasing combines a four-point polyBLAMP correction derived from runtime Bézier tangents with adaptive oversampling, adapting the correction geometrically to general vertex configurations without per-shape analytical derivation.

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The oscillatory modes of musical instrument sounds are commonly modeled with time-varying sinusoids. Several estimation methods model quasi-stationary oscillations accurately, yielding a high-quality representation. However, nonstationary oscillations such as attack transients are still very challenging to model accurately. In this work, we propose to model musical instrument sounds with polynomial modulation sinusoids (PMS) estimated with the distribution derivative method (DDM). DDM gives accurate parameter estimations for PMS with arbitrary order, allowing great flexibility in modeling temporal changes inside analysis frames as amplitude and frequency modulations. We used 39 musical instrument sounds to compare DDM objectively against the standard (SM+) and an adaptive sinusoidal model (eaQHM) using time and frequency error measures. We showed that DDM captures more oscillatory energy than SM+ or eaQHM by modeling PMS more accurately. A MUSHRA listening test with 18 selected sounds confirmed that DDM has higher perceptual quality than both SM+ and eaQHM and that DDM is almost perceptually indistinguishable from the original sounds.

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The Möbius strip is perhaps the most recognizable topological object of general knowledge. It can be described mathematically in various ways including the formalism of line bundles. In this paper we discuss the bundle idea in the context of digital processing over a circular buffer and show how the idea leads to a more general notion known as monodromy, which describes the effect of the space on traversing a circle once. In this formulation, the monodromy of the Möbius strip is an orientation inversion characterized by a change in sign. This in turns leads to the concept of the winding number, which describes how many windings it takes to return to the original state. We discuss variable monodromy and illustrate that the winding number is robust under this variation. This will allow us to interpret previous disparate results in audio signal processing from Möbius waveguides to chaotic oscillators in delay loops in one unified framework. We close by showing how extending from line to vector bundles opens up the notion of braids to describe monodromy.

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