Download Non-iterative Modal Parameter Estimation for Plate Reverbs via Matrix-Pencil-Guided State Space Model Initialization Modal parameter identification for plate reverbs remains a challenging problem in virtual-analog audio effect emulation. Though neural network-based black-box approaches achieve high modeling accuracy, they generally lack interpretability and do not provide access to physically meaningful modal parameters. In this work, we present our solution to Task B of the DAFx Plate Reverb Parameter Estimation Challenge. Our method first estimates the total number of modes and then employs a Matrix Pencil (MP)-guided eigenvalue initialization strategy for a diagonal complex-valued State Space Model (SSM), which can be interpreted as a bank of parallel second-order all-pole filters. Exploiting the linearity of the resulting system, we compute the state impulse responses and replace gradient-based optimization with a closed-form least-squares estimation of the modal gains. The proposed approach enables accurate recovery of the modal parameters while maintaining an interpretable system representation.
Download Adapting Diffusion-Based Music Synthesis to Speech and Singing Voice Conversion Recent diffusion-based generative models have achieved strong results in domain-specific audio generation tasks such as speech, singing, and instrumental music synthesis. However, these models are typically specialized and do not generalize well to mixed or intermediate audio types. In this work, we adapt a diffusion-based model originally designed for multi-instrument music synthesis to voice conversion, covering both speech and singing within a unified framework. Specifically, we extend musical note-based conditioning to include phonetic posteriorgrams (PPGs) and pitch contours, and reinterpret timbre conditioning as speaker or singer identity via feature-wise linear modulation. Experiments show that the adapted model matches or surpasses a dedicated voice conversion system in terms of naturalness and performer similarity, while maintaining accurate pitch control across speech and singing. At the same time, we observe limitations in phonetic fidelity and a degradation in vocal quality when incorporating instrumental training data. Furthermore, we demonstrate that off-the-shelf feature extractors provide effective conditioning signals, enabling large-scale self-supervised training without manual annotations. These results highlight the potential of cross-domain model transfer towards unified audio generation systems capable of handling speech, singing, and music.
Download DAFx Challenge Introduction & Results The 1st DAFx Parameter Estimation Challenge is an open initiative to advance the state of the art in parameter estimation for acoustic modeling. Stated as a system identification problem, this first edition focuses on plate reverberation—an archetypal dense, modal and weakly damped acoustic system. Participants tackled two tasks: (A) estimating the physical parameters of a vibrating plate from its impulse response, and (B) recovering the modal parameters of the same system. Both rest on a simulation framework based on the damped Kirchhoff–Love plate equation, and both are posed and scored entirely on synthetic data produced by that framework: no measurement of a real plate is involved. Two participants solved Task A down to machine precision by different strategies: one a neural network trained on a very large dataset, and one gradient-free optimization with many inexpensive evaluations. Task B proved considerably harder: the best submission attains a relative error of 0.33 on a [0, 2] scale, and every method recovers modal frequencies and decay rates far more accurately than modal gains. A complementary frequency-domain evaluation reorders the ranking and exposes a systematic gain bias to which the per-mode metric is blind.
Download Diffusion-Based Music Audio Editing System Using Differentiable Digital Signal Processing Mixture Model This paper proposes a music audio editing system that enables source-wise editing of harmonic instrument mixtures without explicit source separation. It builds on our previously proposed score-informed method for estimating source-wise synthesis parameters, i.e., time-varying controls used to synthesize each source, such as fundamental frequency and loudness. The method directly estimates these parameters from a mixture signal and the corresponding musical score in an analysis-by-synthesis framework. Using the estimated parameters, the proposed system allows users to edit individual sources by modifying note sequences and instrument types, and then re-synthesizes the edited mixture. Through demonstrations on two-instrument mixtures, we show that the system supports note-level phrasing modification and instrument conversion of selected sources.
Download Evaluating Dynamic Range Compressor Models Using Control-Voltage Measurements: An Approach and Dataset The quantity that defines the behavior of a dynamic range compressor is the time-varying gain applied to the signal as a function of the input level. However, models of these devices are typically evaluated using proxy metrics because isolating the gain reduction signal from the audio input–output data included in existing datasets creates an ill-conditioned inverse problem. It is unclear how accurately these metrics describe the behavior the model is tasked with emulating, particularly as waveform-based metrics can be influenced by secondary effects introduced by analog processing and capture, even when those effects are inaudible. We investigate a method of evaluation in which the gain-reduction signal produced by a model is measured directly against a gain-reduction control voltage signal produced by the hardware. To evaluate the efficacy of this metric as a learning objective, a gray-box model is trained using loss computed directly over the gain control signals alongside two models trained using common proxy losses. The models trained using proxy losses did not achieve parity with models trained directly on the gain control signal when evaluated with respect to the underlying control trajectory, and the waveform-domain metrics assigned similar errors to models that were clearly separated by the direct metric. To facilitate further exploration of this method of evaluation, we present a Solid State Logic bus compressor dataset that includes the gain control voltage signal captured alongside the audio output.
Download Perceptually Motivated Alignment and Interpolation of Pitch-Aligned Time-Frequency Representations This paper proposes methods for alignment and interpolation of pitch-aligned time-frequency representations, building on the tonal interval vector. Extensions reformulate it as an invertible operator, enabling alignment via permutation search under perceptually weighted distances and interpolation via optimal transport with a circular formulation respecting harmonic structure. The work also develops a geometric scale representation factorizing scale structure into root, density, and color.
Download Peak-Residual Modal Estimation with Learned Calibration and High-Band Density Correction ★ This paper describes two related submissions to Task B of the 1st DAFx Parameter Estimation Challenge. Both estimate modal frequency, decay, and gain directly from an unnormalised plate impulse response without using plate parameters, the excluded analytical modal-frequency law, or official-test ground truth. The primary system constructs a large candidate pool through prominence-graded spectral peak picking, iterative residual analysis, multi-view consensus, band-wise budgeting, and a learned file-level mode-count target. Raw decay and gain estimates are then corrected by a small mode-wise neural network that is not allowed to move frequencies or change the number of rows. A secondary variant addresses suspected high-frequency under-counting with a separately gated, non-oracle density-fill stage in the 6–10 kHz band. The paper reports development diagnostics, reproducibility information, and descriptive statistics for the 16 official outputs. The two variants expose a deliberate precision–recall trade-off: one preserves a visible spectral justification for every row, while the other tests bounded hidden-multiplicity augmentation in densely overlapped regions.
Download ALAMODE: Automated Learning of Acoustical Modal Parameters via Differential Evolution This paper is a technical report on the methodology submitted for Task A of the 1st DAFx Parameter Estimation Challenge. The goal of the challenge’s task is to invert the multi-dimensional physical and geometric parameters of a virtual plate reverberator given a target reference impulse response. To achieve this, we present a multi-stage gradient-free optimization framework. This three-stage optimization is computed using an efficient physics-based simulator, starting with an optimization of only mode frequency-determining physical parameters, followed by a 6-DoF parameter optimization with position-determining ones and a final phase for frequency- and position-independent mode amplitude estimation.
Download Performance-Oriented Wave Digital Circuit Emulation Wave Digital Filters are a circuit-modeling paradigm well-suited for reusable software implementation, but existing software implementations often incur significant overhead due to run-time abstractions and data layout constraints. This paper presents a performance-oriented toolchain for implementing Wave Digital circuit models based on static code generation. The toolchain consists of a declarative circuit description language, a compiler that generates circuit simulation code with minimal persistent state and no run-time abstraction, and a minimal runtime library implementing specialized circuit components as Wave Digital Filters. Performance measurements across several test circuits demonstrate that the generated models consistently outperform existing implementations, and achieve near-ideal performance relative to a theoretical execution bound.
Download A Dual-Stream Framework Combining Audio Spectrogram Transformer and Dynamic Mode Decomposition for Plate Modal Parameter Estimation ★ Plate reverberation is characterized by a dense distribution of resonant modes, which makes the estimation of modal parameters from observed responses a challenging inverse problem. To address this problem, we propose a physics guided dual stream framework that integrates an Audio Spectrogram Transformer (AST) with Dynamic Mode Decomposition (DMD). The AST branch models the global temporal and spectral structure of the impulse response, whereas the DMD branch extracts local descriptors associated with modal dynamics. The resulting representations are fused and processed by convolutional prediction heads to jointly estimate mode presence and the corresponding modal parameters. Experiments on the official validation set of Task B in the DAFx Challenge show that the proposed method reduces the overall relative error from 1.976 for the official baseline to 0.867. These results demonstrate that integrating local dynamic information derived from physical modeling with global transformer based representations substantially improves plate modal parameter estimation.