Download A Statistics-Driven Differentiable Approach for Sound Texture Synthesis and Analysis In this work, we introduce TexStat, a novel loss function specifically designed for the analysis and synthesis of texture sounds
characterized by stochastic structure and perceptual stationarity.
Drawing inspiration from the statistical and perceptual framework
of McDermott and Simoncelli, TexStat identifies similarities
between signals belonging to the same texture category without
relying on temporal structure. We also propose using TexStat
as a validation metric alongside Frechet Audio Distances (FAD) to
evaluate texture sound synthesis models. In addition to TexStat,
we present TexEnv, an efficient, lightweight and differentiable
texture sound synthesizer that generates audio by imposing amplitude envelopes on filtered noise. We further integrate these components into TexDSP, a DDSP-inspired generative model tailored
for texture sounds. Through extensive experiments across various
texture sound types, we demonstrate that TexStat is perceptually meaningful, time-invariant, and robust to noise, features that
make it effective both as a loss function for generative tasks and as
a validation metric. All tools and code are provided as open-source
contributions and our PyTorch implementations are efficient, differentiable, and highly configurable, enabling its use in both generative tasks and as a perceptually grounded evaluation metric.
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 Feature design for the classification of audio effect units by input/output measurements Virtual analog modeling is an important field of digital audio signal processing. It allows to recreate the tonal characteristics of real-world sound sources or to impress the specific sound of a certain analog device upon a digital signal on a software basis. Automatic virtual analog modeling using black-box system identification based on input/output (I/O) measurements is an emerging approach, which can be greatly enhanced by specific pre-processing methods suggesting the best-fitting model to be optimized in the actual identification process. In this work, several features based on specific test signals are presented allowing to categorize instrument effect units into classes of effects, like distortion, compression, modulations and similar categories. The categorization of analog effect units is especially challenging due to the wide variety of these effects. For each device, I/O measurements are performed and a set of features is calculated to allow the classification. The features are computed for several effect units to evaluate their applicability using a basic classifier based on pattern matching.
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 Simulation of Textured Audio Harmonics Using Random Fractal Phaselets We present a method of simulating audio signals using the principles of random fractal geometry which, in the context of this paper, is concerned with the analysis of statistically self-affine ‘phaselets’. The approach is used to generate audio signals that are characterised by texture and timbre through the Fractal Dimension such as those associated with bowed stringed instruments. The paper provides a short overview on potential simulation methods using Artificial Neural Networks and Evolutionary Computing and on the problems associated with using a deterministic approach based on solutions to the acoustic wave equation. This serves to quantify the origins of the ‘noise’ associated with multiple scattering events that characterise texture and timbre in an audio signal. We then explore a method to compute the phaselet of a phase signal which is the primary phase function from which a phase signal is, to a good approximation, a periodic replica and show that, by modelling the phaselet as a random fractal signal, it can be characterised by the Fractal Dimension. The Fractal Dimension is then used to synthesise a phaselet from which the phase function is computed through multiple concatenations of the phaselet. The paper provides details of the principal steps associated with the method considered and examines some example results, providing a URL to m-coded functions for interested readers to repeat the results obtained and develop the algorithms further.
Download New Method for Analysis and Modeling of Nonlinear Audio Systems In this paper a new method for analysis and modeling of nonlinear audio systems is presented. The method is based on swept-sine excitation signal and nonlinear convolution firstly presented in [1, 2]. It can be used in nonlinear processing for audio applications, to simulate analog nonlinear effects (distortion effects, limiters) in digital domain.
Download A jump start for NMF with N-FINDR and NNLS Nonnegative Matrix Factorization is a popular tool for the analysis of audio spectrograms. It is usually initialized with random data, after which it iteratively converges to a local optimum. In this paper we show that N-FINDR and NNLS, popular techniques for dictionary and activation matrix learning in remote sensing, prove useful to create a better starting point for NMF. This reduces the number of iterations necessary to come to a decomposition of similar quality. Adapting algorithms from the hyperspectral image unmixing and remote sensing communities, provides an interesting direction for future research in audio spectrogram factorization.
Download Radial Basis Function Networks for conversion of sound spectra In many high-level signal processing tasks, such as pitch shifting, voice conversion or sound synthesis, accurate spectral processing is required. Here, the use of Radial Basis Function Networks (RBFN) is proposed for the modeling of the spectral changes (or conversions) related to the control of important sound parameters, such as pitch or intensity. The identification of such conversion functions is based on a procedure which learns the shape of the conversion from few couples of target spectra from a data set. The generalization properties of RBFNs provides for interpolation with respect to the pitch range. In the construction of the training set, mel-cepstral encoding of the spectrum is used to catch the perceptually most relevant spectral changes. The RBFN conversion functions introduced are characterized by a perceptually-based fast training procedure, desirable interpolation properties and computational efficiency.
Download An Efficient Pitch-Tracking Algorithm Using A Combination Of Fourier Transforms In this paper we present a technique for detecting the pitch of sound using a series of two forward Fourier transforms. We use an enhanced version of the Fourier transform for a better accuracy, as well as a tracking strategy among pitch candidates for an increased robustness. This efficient technique allows us to precisely find out the pitches of harmonic sounds such as the voice or classic musical instruments, but also of more complex sounds like rippled noises.