Paper Archive

We present InstructFX2FX, a system for sequential audio effect refinement through multi-turn natural-language instructions. Existing text-to-effect systems are largely single-shot, mapping one textual descriptor to one preset. Real audio engineering is instead sequential: engineers refine an existing effect chain through successive instructions. This poses a stateful problem that single-shot systems do not address: given the current effect parameters state and a new instruction, update the sound while preserving what earlier instructions already achieved. InstructFX2FX addresses this with a hybrid architecture that divides labor between a language model and CLAP-guided optimization. The LLM serves as a high-level planner that selects effects and proposes the initial parameter state, motivated by recent evidence that LLMs can outperform CLAP-based optimization for single-turn text-to-effect mapping; CLAP-guided optimization then refines the existing parameter state, providing a more stable and robust refinement mechanism than LLM reprompting. In the demo, attendees drive a dry recording through successive natural-language instructions: after each turn, they choose how strongly the effect is applied, then issue the next instruction based on what still differs from the sound they intend. In a preliminary evaluation on SocialFX-derived descriptor pairs, CLAP-guided refinement achieves lower DSP-feature MMD than an LLM+LLM initialize-then-reprompt baseline on 9 of 10 pairs. Trajectory analysis further shows that, for differentiable effects, optimization tends to gradually move the audio toward the new target while retaining the effects of the previous instruction, highlighting the potential for gradual refinement. Audio demo and source code are available online.

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Overlap-add (OLA) is the simplest approach to audio time stretching. Methods like the phase vocoder (PV) and waveform-similarity OLA (WSOLA) offer higher quality results but require operations like the FFT or cross-correlation. On low power embedded hardware, this cost adds up quickly. We present a content-adaptive OLA method, an order of magnitude cheaper than PV or WSOLA, whose dominant artifacts are added saturation and some spectral contrast loss. Our method reduces uniformly sampled signals to sets of timestamped local extrema, a sparse representation where the distance between points encodes the signal's information density directly into the buffer. In OLA, the crossfade duration is fixed, but no one value suits both transients and sustained sounds. We use the extrema density to inform the crossfade duration, adapting it to the signal's local content on a sample-by-sample basis. We compare our method against OLA, WSOLA, and PV using objective metrics and a listening test. Our method coherently stretches audio, preserving transients across a wide range of stretch ratios and capturing dense, layered material cleanly.

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This work introduces a novel framework for Concatenation-Driven Convolution (CDC), unifying concatenative synthesis and real-time convolution into a single integrated audio processing paradigm. While concatenative synthesis has traditionally been used for corpus-based sound generation and convolution has served as a largely static filtering technique, the proposed approach reconceptualizes impulse responses (IRs) as dynamic, navigable sonic material. In the CDC framework, a corpus of audio segments is analyzed using perceptual features and organized via a self-organizing map (SOM), enabling intuitive, gesture-based traversal of a structured timbral space; the resulting concatenative output is treated as a continuously evolving impulse response and injected directly into a partitioned convolution engine. Its central technical contribution is single-engine frequency-domain kernel interpolation: rather than crossfading the outputs of two convolution engines, the FFT-domain kernels of the current and target IRs are interpolated within a single engine, preserving the internal convolution state across IR transitions and avoiding the warm-up energy loss inherent to dual-engine crossfading.

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Shimmer reverberation is an effect used in music production to deliver ethereal, pitch-shifted textures and evolving ambient soundscapes. This paper explores the synthesis of shimmer effects using the feedback delay network architecture, a popular real-time reverberator. We propose five distinct approaches for integrating nonlinear and time-varying operations into the feedback loop, focusing on expanding the harmonic content while adhering to energy-preservation and stability criteria. Our approach can generate a wide range of sonic characteristics, from harmonically rich distortions to musically coherent pitch-shifted reverberation, while maintaining stability and controllable decay behavior.

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This paper presents a framework for designing creative reverbs using parametric orthogonal feedback matrices on Feedback Delay Networks (FDNs) through recursive Kronecker products of 2D rotation and reflection matrices. By parameterizing each 2×2 kernel with a single angle, we construct a family of 2M×2M orthogonal matrices that maintain losslessness while enabling continuous control over network topology. We then exploit their recursive definition to compute the feedback operation with an O(N log₂ N) divide-and-conquer algorithm that matches the Fast Walsh-Hadamard Transform time complexity while offering parametric flexibility. Strategic manipulation of individual kernel angles enables creative sound design applications, such as stereo cross-coupling, selective freeze, and time-varying modulation for resonance breaking.

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Artificial reverberation can be produced either by convolving a signal with a measured room impulse response (RIR) or by synthesizing it with a parametric algorithm such as a Feedback Delay Network (FDN). The former reproduces a captured space faithfully but is costly to run and offers no control over its acoustic properties, while the latter is efficient and editable but hard to match to a specific room. In this paper we bridge the two by fitting a fully differentiable FDN to a measured RIR through gradient descent. The proposed network uses sixteen delay lines at a sampling rate of 48 kHz and trains all of its components jointly, including the delay lengths, the feedback matrix, the early-reflection taps, and a set of attenuation filters that control the frequency-dependent decay.

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The Giant FFT is a single DFT spanning an entire audio file that produces a spectrum encoding the complete temporal evolution of a sound. Creative manipulations in this domain have produced compelling results, but typically smear discrete events into sustained textures by disrupting the temporal relationships between frequency bins. This paper introduces a framework for coherent spectral manipulation in the "group delay domain", where the derivative of the phase spectrum with respect to frequency makes the temporal center of gravity of spectral energy explicit at every frequency bin. By identifying spectral regions around amplitude peaks and grouping them by group delay similarity, spectral features can be displaced in time through uniform modification of their group delay.

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A parametric modal reverberator is presented in which synthesis parameters are derived from a large, curated corpus of room impulse responses (IRs). The collected responses are subjected to modal decomposition, yielding per-mode frequencies, damping coefficients, and residue amplitudes, together with a short early-reflection finite impulse response (FIR) filter. From the decomposed data, a feature table is constructed per IR comprising standard acoustic indices, per-band damping and density statistics, amplitude distributions, and FIR descriptors—50 variables in total. Six acoustically meaningful user controls are selected; since these exhibit substantial pairwise correlations across the corpus, they are orthogonalised via principal component analysis (PCA) prior to regression.

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Accurate and interpretable modeling of plate reverbs remains an important challenge in virtual analog modeling of audio effects. While existing neural network-based black-box approaches already achieve high-quality synthesis and strong perceptual quality, they often lack the possibility to identify the underlying physically meaningful complex, long-memory modal behavior. In this work, we address this limitation by proposing a restricted complex-valued diagonal State Space Model (SSM), showing its equivalence to a parallel second-order all-pole filter, also utilizing efficient training via parallel state computation using the parallel scan algorithm. Additionally, we propose a Matrix Pencil (MP) guided eigenvalue initialization, improving synthesis quality and system identification performance.

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Physical modeling sound synthesis has achieved remarkable success in terms of its fidelity to reality. In many cases, since modeling of the physical system is performed on the sounding objects that already exist in the real world, observation precedes the model itself. Departing from this convention, this paper aims to physically model the acoustic characteristics of objects that do not necessarily exist in reality. Specifically, we study wave propagation on compact two-dimensional (2D) manifolds that are non-orientable surfaces, such as the Klein bottle that cannot be embedded in three-dimensional Euclidean space without self-intersection. We derive closed-form expressions for the eigenfrequencies and mode shapes of non-orientable 2D topologies and study their acoustic characteristics. The modal structures are verified through comparison with finite-difference time-domain simulations. The results demonstrate how the topological character formed by the boundaries influences the acoustic resonances, and how the quotient-space framework provides a practical route to reverb synthesis on geometries with no physical counterpart.

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