Paper Archive

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.

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Neural networks have been applied within the Wave Digital Filter (WDF) framework as data-driven models for nonlinear multi-port circuit elements. Conventionally, these models are trained on wave variables obtained by sampling the current-voltage characteristic of the considered nonlinear element before being incorporated into the circuit WDF implementation. However, isolating multi-port elements for this process can be challenging, as their nonlinear behavior often depends on dynamic effects that emerge from interactions with the surrounding circuit. In this paper, we propose a novel approach for training neural models of nonlinear multi-port elements directly within a circuit’s Wave Digital (WD) discretetime implementation, relying solely on circuit input-output voltage measurements. Exploiting the differentiability of WD simulations, we embed the neural network into the simulation process and optimize its parameters using gradient-based methods by minimizing a loss function defined over the circuit output voltage. Experimental results demonstrate the effectiveness of the proposed approach in accurately capturing the nonlinear circuit behavior, while preserving the interpretability and modularity of WDFs.

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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.

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Component-wise circuit modeling, also known as “white-box” modeling, is a well established and much discussed technique in virtual analog modeling. This approach is generally limited in accuracy by lack of access to the exact component values present in a real example of the circuit. In this paper we show how this problem can be addressed by implementing the white-box model in a differentiable form, and allowing approximate component values to be learned from raw input–output audio measured from a real device.

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The ResNet-18 image classification model is employed to determine the physical parameters of a plate reverb from a recording of the impulse response. The model is adapted to derive parameters using normalized and down-sampled multi-resolution spectrograms computed from the provided impulse responses (IRs). To refine the prediction of the output location, the spectral phase response is also included as an additional input channel to the network since multiple output locations can give the same magnitude response for high-order resonant modes. On a 5000 IR validation set, our model achieves an average normalized mean squared error (NMSE) of 0.02920 across all parameters, with the lowest average NMSE occurring for parameters yo (0.00228), Ly (0.00347), and xo (0.00574).

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Many recent approaches to creative transformations of musical audio have been motivated by the success of raw audio generation models such as WaveNet, in which audio samples are modeled by generative neural networks. This paper describes a generative audio synthesis model for multi-drum translation based on a WaveNet denosing autoencoder architecture. The timbre of an arbitrary source audio input is transformed to sound as if it were played by various percussive instruments while preserving its rhythmic structure. Two evaluations of the transformations are conducted based on the capacity of the model to preserve the rhythmic patterns of the input and the audio quality as it relates to timbre of the target drum domain. The first evaluation measures the rhythmic similarities between the source audio and the corresponding drum translations, and the second provides a numerical analysis of the quality of the synthesised audio. Additionally, a semi- and fully-automatic audio effect has been proposed, in which the user may assist the system by manually labelling source audio segments or use a state-of-the-art automatic drum transcription system prior to drum translation.

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The noise that lavalier microphones produce when rubbing against clothing (typically referred to as rustle) can be extremely difficult to automatically remove because it is highly non-stationary and overlaps with speech in both time and frequency. Recent breakthroughs in deep neural networks have led to novel techniques for separating speech from non-stationary background noise. In this paper, we apply neural network speech separation techniques to remove rustle noise, and quantitatively compare multiple deep network architectures and input spectral resolutions. We find the best performance using bidirectional recurrent networks and spectral resolution of around 20 Hz. Furthermore, we propose an ambience preservation post-processing step to minimize potential gating artifacts during pauses in speech.

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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 unified visual interface for flexible spectral audio manipulation based on editable lookup tables (LUTs). In the proposed approach, the audio spectrum is visualized as a two-dimensional color map of frequency versus amplitude, serving as an editable lookup table for modifying the sound. This single tool can replicate common audio effects such as equalization, pitch shifting, and spectral compression, while also enabling novel sound transformations through creative combinations of adjustments. By consolidating these capabilities into one visual platform, the system has the potential to streamline audio-editing workflows and encourage creative experimentation. The approach also supports real-time processing, providing immediate auditory feedback in an interactive graphical environment. Overall, this LUT-based method offers an accessible yet powerful framework for designing and applying a broad range of spectral audio effects through intuitive visual manipulation.

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"Audio d-touch" uses a consumer-grade web camera and customizable block objects to provide an interactive tangible interface for a variety of time based musical tasks such as sequencing, drum editing and collaborative composition. Three instruments are presented here. Future applications of the interface are also considered.

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