Paper Archive

Virtual Analog (VA) modeling aims to simulate the behavior of hardware circuits via algorithms to replicate their tone digitally. Dynamic Range Compressor (DRC) is an audio processing module that controls the dynamics of a track by reducing and amplifying the volumes of loud and quiet sounds, which is essential in music production. In recent years, neural-network-based VA modeling has shown great potential in producing high-fidelity models. However, due to the lack of data quantity and diversity, their generalization ability in different parameter settings and input sounds is still limited. To tackle this problem, we present Solid State Bus-Comp, the first large-scale and diverse dataset for modeling the classical VCA compressor — SSL 500 G-Bus. Specifically, we manually collected 175 unmastered songs from the Cambridge Multitrack Library. We recorded the compressed audio in 220 parameter combinations, resulting in an extensive 2528-hour dataset with diverse genres, instruments, tempos, and keys. Moreover, to facilitate the use of our proposed dataset, we conducted benchmark experiments in various open-sourced black-box and grey-box models, as well as white-box plugins. We also conducted ablation studies in different data subsets to illustrate the effectiveness of the improved data diversity and quantity. The dataset and demos are on our project page: https: //www.yichenggu.com/SolidStateBusComp/.

Download

Structured pruning is a technique for reducing the computational load and memory footprint of neural networks by removing structured subsets of parameters according to a predefined schedule or ranking criterion. This paper investigates the application of structured pruning to real-time neural network audio effects, focusing on both feedforward networks and recurrent architectures. We evaluate multiple pruning strategies at inference time, without retraining, and analyze their effects on model performance. To quantify the trade-off between parameter count and audio fidelity, we construct a theoretical model of the approximation error as a function of network architecture and pruning level. The resulting bounds establish a principled relationship between pruninginduced sparsity and functional error, enabling informed deployment of neural audio effects in constrained real-time environments.

Download

This work focuses on developing an artistic tool that performs an unsupervised mapping between text and sound, converting an input text string into a series of sounds from a given sound corpus. With the use of a pre-trained sound embedding model and a separate, pre-trained text embedding model, the goal is to find a mapping between the two feature spaces. Our approach is unsupervised which allows any sound corpus to be used with the system. The tool performs the task of text-to-sound retrieval, creating a soundfile in which each word in the text input is mapped to a single sound in the corpus, and the resulting sounds are concatenated to play sequentially. We experiment with three different mapping methods, and perform quantitative and qualitative evaluations on the outputs. Our results demonstrate the potential of unsupervised methods for creative applications in text-to-sound mapping.

Download

We introduce a novel method for designing attenuation filters in digital audio reverberation systems based on Feedback Delay Networks (FDNs). Our approach uses Second Order Sections (SOS) of Infinite Impulse Response (IIR) filters arranged as parametric equalizers (PEQ), enabling fine control over frequency-dependent reverberation decay. Unlike traditional graphic equalizer designs, which require numerous filters per delay line, we propose a scalable solution where the number of filters can be adjusted. The frequency, gain, and quality factor (Q) parameters are shared parameters across delay lines and only the gain is adjusted based on delay length. This design not only reduces the number of optimization parameters, but also remains fully differentiable and compatible with gradient-based learning frameworks. Leveraging principles of analog filter design, our method allows for efficient and accurate filter fitting using supervised learning. Our method delivers a flexible and differentiable design, achieving state-of-the-art performance while significantly reducing computational cost.

Download

This article presents Partiels, an open-source application developed at IRCAM to analyze digital audio files and explore sound characteristics. The application uses Vamp plug-ins to extract various information on different aspects of the sound, such as spectrum, partials, pitch, tempo, text, and chords. Partiels is the successor to AudioSculpt, offering a modern, flexible interface for visualizing, editing, and exporting analysis results, addressing a wide range of issues from musicological practice to sound creation and signal processing research. The article describes Partiels’ key features, including analysis organization, audio file management, results visualization and editing, as well as data export and sharing options, and its interoperability with other software such as Max and Pure Data. In addition, it highlights the numerous analysis plug-ins developed at IRCAM, based in particular on machine learning models, as well as the IRCAM Vamp extension, which overcomes certain limitations of the original Vamp format.

Download

Machine learning for sound generation is rapidly expanding within the computer music community. However, most datasets used to train models are built from field recordings, foley sounds, instrumental notes, or commercial music. This presents a significant limitation for composers working in acousmatic and electroacoustic music, who require datasets tailored to their creative processes. To address this gap, we introduce the SCHAEFFER Dataset (Spectromorphological Corpus of Human-annotated Audio with Electroacoustic Features For Experimental Research), a curated collection of 1000 sound objects designed and annotated by composers and students of electroacoustic composition. The dataset, distributed under Creative Commons licenses, features annotations combining technical and poetic descriptions, alongside classifications based on pre-defined spectromorphological categories.

Download

This study investigates the perceptual sensitivity to early reflection changes across different spatial directions in a virtual reality (VR) environment. Using an ABX discrimination paradigm, participants evaluated speech stimuli convolved with thirdorder Ambisonic room impulse responses under three position reversal (Left–Right, Front–Back, and Floor–Ceiling) and three reverberation conditions (RT60 = 1.0 s, 0.6 s, and 0.2 s). Binomial tests revealed that participants consistently detected early reflection differences in the Left–Right reversal, while discrimination performance in the other two directions remained at or near chance. This result can be explained by the higher acuity and lower localisation blur found for the human auditory system. A two-way ANOVA confirmed a significant main effect of spatial position (p = 0.00685, η² = 0.1605), with no significant effect of reverberation or interaction. The analysis of the binaural room impulse responses showed wave forms and Direct-ReverberantRatio differences in the Left–Right reversal position, aligning with perceptual results. However, no definitive causal link between DRR variations and perceptual outcomes can yet be established.

Download

Audio effects (AFXs) are essential tools in music production, frequently applied in chains to shape timbre and dynamics. The order of AFXs in a chain plays a crucial role in determining the final sound, particularly when non-linear (e.g., distortion) or timevariant (e.g., chorus) processors are involved. Despite its importance, most AFX-related studies have primarily focused on estimating effect types and their parameters from a wet signal. To address this gap, we formulate AFX chain recognition as the task of jointly estimating AFX types and their order from a wet signal. We propose a neural-network-based method that embeds wet signals into a hyperbolic space and classifies their AFX chains. Hyperbolic space can represent tree-structured data more efficiently than Euclidean space due to its exponential expansion property. Since AFX chains can be represented as trees, with AFXs as nodes and edges encoding effect order, hyperbolic space is well-suited for modeling the exponentially growing and non-commutative nature of ordered AFX combinations, where changes in effect order can result in different final sounds. Experiments using guitar sounds demonstrate that, with an appropriate curvature, the proposed method outperforms its Euclidean counterpart. Further analysis based on AFX type and chain length highlights the effectiveness of the proposed method in capturing AFX order.

Download

Modal methods for simulating vibrations of strings, membranes, and plates are widely used in acoustics and physically informed audio synthesis. However, traditional implementations, particularly for non-linear models like the von Kármán plate, are computationally demanding and lack differentiability, limiting inverse modelling and real-time applications. We introduce a fast, differentiable, GPU-accelerated modal framework built with the JAX library, providing efficient simulations and enabling gradientbased inverse modelling. Benchmarks show that our approach significantly outperforms CPU and GPU-based implementations, particularly for simulations with many modes. Inverse modelling experiments demonstrate that our approach can recover physical parameters, including tension, stiffness, and geometry, from both synthetic and experimental data. Although fitting physical parameters is more sensitive to initialisation compared to methods that fit abstract spectral parameters, it provides greater interpretability and more compact parameterisation. The code is released as open source to support future research and applications in differentiable physical modelling and sound synthesis.

Download

This paper introduces a digital reconstruction of the morphophone, a complex magnetophonic device developed in the 1950s within the laboratories of the GRM (Groupe de Recherches Musicales) in Paris. The analysis, design, and implementation methodologies underlying the Digital Morphophone Environment are discussed. Based on a detailed review of historical sources and limited documentation – including a small body of literature and, most notably, archival images – the core operational principles of the morphophone have been modeled within the MAX visual programming environment. The main goals of this work are, on the one hand, to study and make accessible a now obsolete and unavailable tool, and on the other, to provide the opportunity for new explorations in computer music and research.

Download