Download Physical Modeling Using Recurrent Neural Networks with Fast Convolutional Layers Discrete-time modeling of acoustic, mechanical and electrical systems is a prominent topic in the musical signal processing literature. Such models are mostly derived by discretizing a mathematical model, given in terms of ordinary or partial differential equations, using established techniques. Recent work has applied the techniques of machine-learning to construct such models automatically from data for the case of systems which have lumped states described by scalar values, such as electrical circuits. In this work, we examine how similar techniques are able to construct models of systems which have spatially distributed rather than lumped states. We describe several novel recurrent neural network structures, and show how they can be thought of as an extension of modal techniques. As a proof of concept, we generate synthetic data for three physical systems and show that the proposed network structures can be trained with this data to reproduce the behavior of these systems.
Download DataRES and PyRES: A Room Dataset and a Python Library for Reverberation Enhancement System Development, Evaluation, and Simulation Reverberation is crucial in the acoustical design of physical
spaces, especially halls for live music performances. Reverberation Enhancement Systems (RESs) are active acoustic systems that
can control the reverberation properties of physical spaces, allowing them to adapt to specific acoustical needs. The performance of
RESs strongly depends on the properties of the physical room and
the architecture of the Digital Signal Processor (DSP). However,
room-impulse-response (RIR) measurements and the DSP code
from previous studies on RESs have never been made open access, leading to non-reproducible results. In this study, we present
DataRES and PyRES—a RIR dataset and a Python library to increase the reproducibility of studies on RESs. The dataset contains RIRs measured in RES research and development rooms and
professional music venues. The library offers classes and functionality for the development, evaluation, and simulation of RESs.
The implemented DSP architectures are made differentiable, allowing their components to be trained in a machine-learning-like
pipeline. The replication of previous studies by the authors shows
that PyRES can become a useful tool in future research on RESs.
Download Differentiable Active Acoustics - Optimizing Stability via Gradient Descent Active acoustics (AA) refers to an electroacoustic system that actively modifies the acoustics of a room. For common use cases, the number of transducers—loudspeakers and microphones—involved in the system is large, resulting in a large number of system parameters. To optimally blend the response of the system into the natural acoustics of the room, the parameters require careful tuning, which is a time-consuming process performed by an expert. In this paper, we present a differentiable AA framework, which allows multi-objective optimization without impairing architecture flexibility. The system is implemented in PyTorch to be easily translated into a machine-learning pipeline, thus automating the tuning process. The objective of the pipeline is to optimize the digital signal processor (DSP) component to evenly distribute the energy in the feedback loop across frequencies. We investigate the effectiveness of DSPs composed of finite impulse response filters, which are unconstrained during the optimization. We study the effect of multiple filter orders, number of transducers, and loss functions on the performance. Different loss functions behave similarly for systems with few transducers and low-order filters. Increasing the number of transducers and the order of the filters improves results and accentuates the difference in the performance of the loss functions.
Download One-to-Many Conversion for Percussive Samples A filtering algorithm for generating subtle random variations in
sampled sounds is proposed. Using only one recording for impact
sound effects or drum machine sounds results in unrealistic repetitiveness during consecutive playback. This paper studies spectral
variations in repeated knocking sounds and in three drum sounds:
a hihat, a snare, and a tomtom. The proposed method uses a short
pseudo-random velvet-noise filter and a low-shelf filter to produce
timbral variations targeted at appropriate spectral regions, yielding potentially an endless number of new realistic versions of a
single percussive sampled sound.
The realism of the resulting
processed sounds is studied in a listening test. The results show
that the sound quality obtained with the proposed algorithm is at
least as good as that of a previous method while using 77% fewer
computational operations. The algorithm is widely applicable to
computer-generated music and game audio.
Download Differentiable Feedback Delay Network for Colorless Reverberation Artificial reverberation algorithms often suffer from spectral coloration, usually in the form of metallic ringing, which impairs the perceived quality of sound. This paper proposes a method to reduce the coloration in the feedback delay network (FDN), a popular artificial reverberation algorithm. An optimization framework is employed entailing a differentiable FDN to learn a set of parameters decreasing coloration. The optimization objective is to minimize the spectral loss to obtain a flat magnitude response, with an additional temporal loss term to control the sparseness of the impulse response. The objective evaluation of the method shows a favorable narrower distribution of modal excitation while retaining the impulse response density. The subjective evaluation demonstrates that the proposed method lowers perceptual coloration of late reverberation, and also shows that the suggested optimization improves sound quality for small FDN sizes. The method proposed in this work constitutes an improvement in the design of accurate and high-quality artificial reverberation, simultaneously offering computational savings.
Download The Role of Modal Excitation in Colorless Reverberation A perceptual study revealing a novel connection between modal
properties of feedback delay networks (FDNs) and colorless reverberation is presented. The coloration of the reverberation tail
is quantified by the modal excitation distribution derived from the
modal decomposition of the FDN. A homogeneously decaying allpass FDN is designed to be colorless such that the corresponding narrow modal excitation distribution leads to a high perceived
modal density. Synthetic modal excitation distributions are generated to match modal excitations of FDNs. Three listening tests
were conducted to demonstrate the correlation between the modal
excitation distribution and the perceived degree of coloration. A
fourth test shows a significant reduction of coloration by the colorless FDN compared to other FDN designs. The novel connection of modal excitation, allpass FDNs, and perceived coloration
presents a beneficial design criterion for colorless artificial reverberation.
Download Perceptual Decorrelator Based on Resonators Decorrelation filters transform mono audio into multiple decorrelated copies. This paper introduces a novel decorrelation filter design based on a resonator bank, which produces a sum of over a thousand exponentially decaying sinusoids. A headphone listening test was used to identify the minimum inter-channel time delays that perceptually match ERB-filtered coherent noise to corresponding incoherent noise. The decay rate of each resonator is set based on a group delay profile determined by the listening test results at its corresponding frequency. Furthermore, the delays from the test are used to refine frequency-dependent windowing in coherence estimation, which we argue represents the perceptually most accurate way of assessing interaural coherence. This coherence measure then guides an optimization process that adjusts the initial phases of the sinusoids to minimize the coherence between two instances of the resonator-based decorrelator. The delay results establish the necessary group delay per ERB for effective decorrelation, revealing higher-than-expected values, particularly at higher frequencies. For comparison, the optimization is also performed using two previously proposed group-delay profiles: one based on the period of the ERB band center frequency and another based on the maximum group-delay limit before introducing smearing. The results indicate that the perceptually informed profile achieves equal decorrelation to the latter profile while smearing less at high frequencies. Overall, optimizing the phase response of the proposed decorrelator yields significantly lower coherence compared to using a random phase.