Download Assessing the Effect of Adaptive Music on Player Navigation in Virtual Environments Through this research, we develop a study aiming to explore how adaptive music can help in guiding players across virtual environments. A video game consisting of a virtual 3D labyrinth was built, and two groups of subjects played through it, having the goal of retrieving a series of objects in as short a time as possible. Each group played a different version of the prototype in terms of audio: one had the ability to state their preferences by choosing several musical attributes, which would influence the actual spatialised music they listened to during gameplay; the other group played a version of the prototype with a default, non-adaptive, but also spatialised soundtrack. Time elapsed while completing the task was measured as a way to test user performance. Results show a statistically significant correlation between player performance and the inclusion of a soundtrack adapted to each user. We conclude that there is an absence of a firm musical criteria when making sounds be prominent and easy to track for users, and that an adaptive system like the one we propose proves useful and effective when dealing with a complex user base.
Download Real-Time Modal Synthesis of Crash Cymbals with Nonlinear Approximations, Using a GPU We apply modal synthesis to create a virtual collection of crash cymbals. Synthesizing each cymbal may require enough modes to stress a modern CPU, so a full drum set would certainly not be tractable in real-time. To work around this, we create a GPU-accelerated modal filterbank, with each individual set piece allocated over two thousand modes. This takes only a fraction of available GPU floating-point throughput. With CPU resources freed up, we explore methods to model the different instrument response in the linear/harmonic and non-linear/inharmonic regions that occur as more energy is present in a cymbal: a simple approach, yet one that preserves the parallelism of the problem, uses multisampling, and a more physically-based approach approximates modal coupling.
Download Non-Iterative Schemes for the Simulation of Nonlinear Audio Circuits In this work, a number of numerical schemes are presented in the
context of virtual-analog simulation. The schemes are linearlyimplicit in character, and hence directly solvable without iterative
methods. Schemes of increasing order of accuracy are constructed,
and convergence and stability conditions are proven formally. The
schemes are able to handle stiff problems very efficiently, because
of their fast update, and can be run at higher sample rates to reduce
aliasing. The cases of the diode clipper and ring modulator are
investigated in detail, including several numerical examples.
Download Subjective Evaluation of Sound Quality and Control of Drum Synthesis with Stylewavegan In this paper we investigate into perceptual properties of StyleWaveGAN, a drum synthesis method proposed in a previous publication. For both, the sound quality as well as the control precision StyleWaveGAN has been shown to deliver state of the art performance for quantitative metrics (FAD and MSE of the control parameters). The present paper aims to provide insight into the perceptual relevance of these results. Accordingly, we performed a subjective evaluation of the sound quality as well as a subjective evaluation of the precision of the control using timbre descriptors from the AudioCommons toolbox. We evaluate the sound quality with mean opinion score and make measurements of psychophysical response to the variations of the control. By means of the perceptual tests, we demonstrate that StyleWaveGAN produces better sound quality than state-of-the-art model DrumGAN and that the mean control error is lower than the absolute threshold of perception at every point of measurement used in the experiment.
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 DDSP-Based Neural Waveform Synthesis of Polyphonic Guitar Performance From String-Wise MIDI Input We explore the use of neural synthesis for acoustic guitar from string-wise MIDI input. We propose four different systems and compare them with both objective metrics and subjective evaluation against natural audio and a sample-based baseline. We iteratively develop these four systems by making various considerations on the architecture and intermediate tasks, such as predicting pitch and loudness control features. We find that formulating the control feature prediction task as a classification task rather than a regression task yields better results. Furthermore, we find that our simplest proposed system, which directly predicts synthesis parameters from MIDI input performs the best out of the four proposed systems. Audio examples and code are available.
Download Differentiable MIMO Feedback Delay Networks for Multichannel Room Impulse Response Modeling Recently, with the advent of new performing headsets and goggles, the demand for Virtual and Augmented Reality applications has experienced a steep increase. In order to coherently navigate the virtual rooms, the acoustics of the scene must be emulated in the most accurate and efficient way possible. Amongst others, Feedback Delay Networks (FDNs) have proved to be valuable tools for tackling such a task. In this article, we expand and adapt a method recently proposed for the data-driven optimization of single-inputsingle-output FDNs to the multiple-input-multiple-output (MIMO) case for addressing spatial/space-time processing applications. By testing our methodology on items taken from two different datasets, we show that the parameters of MIMO FDNs can be jointly optimized to match some perceptual characteristics of given multichannel room impulse responses, overcoming approaches available in the literature, and paving the way toward increasingly efficient and accurate real-time virtual room acoustics rendering.
Download A Real-Time Approach for Estimating Pulse Tracking Parameters for Beat-Synchronous Audio Effects Predominant Local Pulse (PLP) estimation, an established method for extracting beat positions and other periodic pulse information from audio signals, has recently been extended with an online variant tailored for real-time applications. In this paper, we introduce a novel approach to generating various real-time control signals from the original online PLP output. While the PLP activation function encodes both predominant pulse information and pulse stability, we propose several normalization procedures to discern local pulse oscillation from stability, utilizing the PLP activation envelope. Through this, we generate pulse-synchronous Low Frequency Oscillators (LFOs) and supplementary confidence-based control signals, enabling dynamic control over audio effect parameters in real-time. Additionally, our approach enables beat position prediction, providing a look-ahead capability, for example, to compensate for system latency. To showcase the effectiveness of our control signals, we introduce an audio plugin prototype designed for integration within a Digital Audio Workstation (DAW), facilitating real-time applications of beat-synchronous effects during live mixing and performances. Moreover, this plugin serves as an educational tool, providing insights into PLP principles and the tempo structure of analyzed music signals.
Download Differentiable Scattering Delay Networks for Artificial Reverberation Scattering delay networks (SDNs) provide a flexible and efficient
framework for artificial reverberation and room acoustic modeling. In this work, we introduce a differentiable SDN, enabling
gradient-based optimization of its parameters to better approximate the acoustics of real-world environments. By formulating
key parameters such as scattering matrices and absorption filters
as differentiable functions, we employ gradient descent to optimize an SDN based on a target room impulse response. Our approach minimizes discrepancies in perceptually relevant acoustic
features, such as energy decay and frequency-dependent reverberation times. Experimental results demonstrate that the learned SDN
configurations significantly improve the accuracy of synthetic reverberation, highlighting the potential of data-driven room acoustic modeling.
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.