Download Solid State Bus-Comp: A Large-Scale and Diverse Dataset for Dynamic Range Compressor Virtual Analog Modeling 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 Unsupervised Estimation of Nonlinear Audio Effects: Comparing Diffusion-Based and Adversarial Approaches Accurately estimating nonlinear audio effects without access to
paired input-output signals remains a challenging problem. This
work studies unsupervised probabilistic approaches for solving this
task. We introduce a method, novel for this application, based
on diffusion generative models for blind system identification, enabling the estimation of unknown nonlinear effects using blackand gray-box models. This study compares this method with a
previously proposed adversarial approach, analyzing the performance of both methods under different parameterizations of the
effect operator and varying lengths of available effected recordings. Through experiments on guitar distortion effects, we show
that the diffusion-based approach provides more stable results and
is less sensitive to data availability, while the adversarial approach
is superior at estimating more pronounced distortion effects. Our
findings contribute to the robust unsupervised blind estimation of
audio effects, demonstrating the potential of diffusion models for
system identification in music technology.