Despite the popularity of guitar effects there is very little existing research on classification and parameter estimation of specific plugins or effect units from guitar recordings. In this paper, convolutional neural networks were used for classification and parameter estimation for 13 overdrive, distortion, and fuzz guitar effects. A novel dataset of processed electric guitar samples was assembled, with four sub-datasets consisting of monophonic or polyphonic samples and discrete or...

PlugSonic is a suite of web- and mobile-based applications for the curation and experience of 3D interactive soundscapes and sonic narratives in the cultural heritage context. It was developed as part of the PLUGGY EU project (Pluggable Social Platform for Heritage Awareness and Participation) and consists of two main applications: PlugSonic Sample, to edit and apply audio effects, and PlugSonic Soundscape, to create and experience 3D soundscapes for headphones playback. The audio processing within PlugSonic is based on the Web Audio API and the 3D Tune-In Toolkit, while the mobile exploration of soundscapes in a physical space is obtained using Apple’s ARKit. The main goal of PlugSonic is technology democratisation; PlugSonic users—whether cultural institutions or citizens—are all given the instruments needed to create, process and experience 3D soundscapes and sonic narratives; without the need for specific devices, external tools (software and/or hardware), specialised knowledge or custom development. The aims of this paper are to present the design and development choices, the user involvement processes as well as a final evaluation conducted with inexperienced users on three tasks (creation, curation and experience), demonstrating how PlugSonic is indeed a simple, effective, yet powerful tool.

Audio effects are extensively used at every stage of audio and music content creation. The majority of differentiable audio effects modeling approaches fall into the black-box or gray-box paradigms; and most models have been proposed and applied to nonlinear effects like guitar amplifiers, overdrive, distortion, fuzz and compressor. Although a plethora of architectures have been introduced for the task at hand there is still lack of understanding on the state of the art, since most publications experiment with one type of nonlinear audio effect and a very small number of devices. In this work we aim to shed light on the audio effects modeling landscape by comparing black-box and gray-box architectures on a large number of nonlinear audio effects, identifying the most suitable for a wide range of devices. In the process, we also: introduce time-varying gray-box models and propose models for compressor, distortion and fuzz, publish a large dataset for audio effects research—ToneTwist AFx—that is also the first open to community contributions, evaluate models on a variety of metrics and conduct extensive subjective evaluation. Code and supplementary material are also available.

PlugSonic is a series of web- and mobile-based applications designed to edit samples and apply audio effects (PlugSonic Sample) and create and experience dynamic and navigable soundscapes and sonic narratives (PlugSonic Soundscape). The audio processing within PlugSonic is based on the Web Audio API while the binaural rendering uses the 3D Tune-In Toolkit. Exploration of soundscapes in a physical space is made possible by adopting Apple’s ARKit. The present paper describes the implementation details, the signal processing chain and the necessary steps to curate and experience a soundscape. We also include some metrics and performance details. The main goal of PlugSonic is to give users a complete set of tools, without the need for specific devices, external software and/or hardware specialised knowledge, or custom development, with the idea that spatial audio has the potential to become a readily accessible and easy to understand technology, for anyone to adopt, whether for creative or research purposes.

Low frequency oscillator (LFO) driven audio effects such as phaser, flanger, and chorus, modify an input signal using time-varying filters and delays, resulting in characteristic sweeping or widening effects. It has been shown that these effects can be modeled using neural networks when conditioned with the ground truth LFO signal. However, in most cases, the LFO signal is not accessible and measurement from the audio signal is nontrivial, hindering the modeling process. To address this, we propose a framework capable of extracting arbitrary LFO signals from processed audio across multiple digital audio effects, parameter settings, and instrument configurations. Since our system imposes no restrictions on the LFO signal shape, we demonstrate its ability to extract quasiperiodic, combined, and distorted modulation signals that are relevant to effect modeling. Furthermore, we show how coupling the extraction model with a simple processing network enables training of end-to-end black-box models of unseen analog or digital LFO-driven audio effects using only dry and wet audio pairs, overcoming the need to access the audio effect or internal LFO signal. We make our code available and provide the trained audio effect models in a real-time VST plugin.

Deep learning approaches for black-box modelling of audio effects have shown promise, however, the majority of existing work focuses on nonlinear effects with behaviour on relatively short time-scales, such as guitar amplifiers and distortion. While recurrent and convolutional architectures can theoretically be extended to capture behaviour at longer time scales, we show that simply scaling the width, depth, or dilation factor of existing architectures does not result in satisfactory performance when modelling audio effects such as fuzz and dynamic range compression. To address this, we propose the integration of time-varying feature-wise linear modulation into existing temporal convolutional backbones, an approach that enables learnable adaptation of the intermediate activations. We demonstrate that our approach more accurately captures long-range dependencies for a range of fuzz and compressor implementations across both time and frequency domain metrics. We provide sound examples, source code, and pretrained models to faciliate reproducibility.