Download FM Synthesizer Audio-Parameter Shared Embeddings 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.
Download Simulation-Based Plate-Reverb Parameter Estimation from a Single Impulse Response ★ We present a simulation-trained, non-iterative estimator for Task A of the 1st DAFx Parameter Estimation Challenge. Each unnormalized plate-reverb impulse response is summarized by amplitude, spectral, and decay descriptors, and an ensemble of tree regressors estimates the six target parameters in one pass. Across two independent synthetic validation sets, the normalized models outperform the training-set mean and an earlier raw-regression baseline. On a shared set, the final ensemble also outperforms a single run of the official default PSO at substantially lower inference cost. Since the official labels are hidden, parameter accuracy is measured on simulator-matched data, and the released responses support only audio-side consistency checks. The estimator returns point estimates without uncertainty.
Download Diagonal Complex-Valued State Space Models for System Identification and Modeling of Metal Plate Reverbs 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.
Download Real-Time Neural Audio on Apple Silicon: Benchmarking Inference Frameworks Under Realistic DAW Contention Neural network models are increasingly deployed in audio plugins across a wide range of applications, including amplifier emulation, effects modeling, and synthesis. This paper evaluates widely used inference options including BNNSGraph, RTNeural, LibTorch, ONNX Runtime, and anira on model architectures commonly used in neural audio plugins. The key contribution is moving beyond isolated benchmarks to evaluate performance under realistic DAW contention, constructing mix sessions with configurable plugin loads. Results show that isolated benchmarks can be misleading, and BNNSGraph proves most robust for convolutional models on Apple Silicon.
Download Quality Audio Prototyping: A Prototype System for Unified Sound Retrieval and Procedural Generation This paper presents Quality Audio Prototyping (QAP), a unified prototype system for sound retrieval and procedural generation. The system is designed to support rapid exploration of sound effects through a common interface that combines retrieval from existing audio collections with controllable procedural synthesis. By bringing these two paradigms together, QAP allows users to search for recorded sounds, generate new material, and iteratively refine results within a single workflow. The prototype emphasizes usability, extensibility, and practical sound-design applications, providing a foundation for future work on integrated retrieval and generation systems.
Download SCAPES: Semantically Conditioned Autoregressive Prior for Environmental Sounds This paper presents SCAPES, a semantically conditioned autoregressive prior for environmental sound generation. The system models discrete audio representations using an autoregressive architecture conditioned on semantic information, enabling the generation of environmental sounds that follow user-specified concepts. By learning a prior over audio tokens, SCAPES combines high-level semantic control with detailed temporal modeling. Experimental evaluation investigates the quality, diversity, and semantic consistency of generated sounds, demonstrating the potential of autoregressive priors for controllable environmental sound synthesis.
Download Simulation-based Inference Plate Reverberation Inverse Problems We address Task A of the 1st DAFx Parameter Estimation Challenge, which aims to retrieve the physical parameters of a plate model from an impulse response. To do so, we use the Simulation-Based Inference (SBI) framework, in which we train a neural network to estimate a density over plate parameters given an impulse response, using a dataset generated by the simulator. Inference for a new impulse response then requires only a forward pass through the network, without involving the simulator. For each test observation, we fine-tune a specific network: additional simulation rounds are performed by sampling parameters from the current estimated distribution, simulating the corresponding impulse responses, and fine-tuning to produce the specialized network.
Download Deep Regularized RNNs for Virtual Analog Virtual analog (VA) modeling methods seek to emulate analog audio hardware using digital signal processing (DSP). Modeling approaches fall into three broad categories: white-box methods, which use detailed device knowledge for accurate simulation; gray-box methods that use generic DSP blocks to model the system; and black-box methods, which rely solely on opaque models learned from input–output data. A category of architectures used widely in black-box modeling are recurrent neural networks (RNNs). To model device controls, the control values can be provided as conditioning input to the network. However, when the conditioning is time-varied, the models are susceptible to producing noise artifacts. Regularization of the RNN dynamics significantly reduces these artifacts, though at a loss in modeling accuracy. This paper closes the dynamics regularization quality gap by introducing deep control-conditioned LSTMs and a gammatone filterbank (GFB) loss. Experiments indicate that the proposed method achieves comparable modeling performance as unregularized baselines while avoiding the noise artifacts caused by time-varying control inputs.
Download Benchmarking Integrated GPU Acceleration of Real-Time Neural Audio Inference on Snapdragon This paper investigates whether integrated GPUs on Qualcomm Snapdragon SoCs can accelerate streaming inference of neural audio models. Five models spanning three orders of magnitude in parameter count are benchmarked across three inference approaches (best available CPU, QNN CPU, and QNN GPU). Results reveal when GPU acceleration offers meaningful gains, when per-call overhead negates benefits, and how model size and architecture determine GPU suitability.
Download SEND: A Spatial Event Neural Detector for Intentional Object Motion in Immersive Music Mixing Deciding exactly when to move audio objects in immersive mixes is a labor-intensive artistic task. Current tools react strictly to instantaneous frequency overlaps, lacking the macroscopic awareness required for musically intentional spatial transitions. To model these decisions, we propose SEND (Spatial Event Neural Detector). Its dual-stream architecture analyzes the target track against its background context, combining a Spec-TNT backbone and a Temporal Convolutional Network (TCN) to capture hierarchical spectral features and precise rhythmic cues. Their dynamic interplay is modeled via a novel Cross-Track Gating Interaction (CTGI) mechanism.