Room Impulse Response Simulation Research Articles

Echo-aware room impulse response generation.

In real-time applications, like interactive virtual reality environments, there is a significant need for low-complexity simulation of room impulse responses in highly complex virtual scenes, but this remains a challenging issue. In particular, simulating late reverberation using physically based acoustic modeling requires much computational effort, contrary to the early reflections that can be modeled by simpler techniques, e.g., the image source method. To tackle this computational complexity issue, we propose a neural network-based hybrid artificial reverberation framework (Echo2Reverb) that generates late reverberation from given early reflections. The proposed model can control both temporal texture and frequency-dependent energy decay, i.e., echo density and spectral energy distribution, of the generated reverberations by extracting spectral and echo-related features and filtering sampled sparse sequences and Gaussian noises using estimated features. To support the end-to-end training with controlled echo density, a differentiable approximation of the normalized echo density profile is proposed. We train and test the model not only for nearly diffuse but also distinct echoes prominent in late reverberations, such as with flutter echoes in narrow corridors. Evaluation results demonstrate that the proposed model can accurately reproduce frequency-dependent energy decay and temporal texture of a room impulse response using only early reflections.

Multi‐GPU room response simulation with hardware raytracing

SummaryTime‐of‐flight camera systems are an essential component in 3D scene analysis and reconstruction for many modern computer vision applications. The development and validation of such systems require testing in a large variety of scenes and situations. Accurate room impulse response simulation greatly speeds up development and validation, as well as reducing its cost, but large computational overhead has so far limited its applicability. While the overall algorithmic requirements of this simulation differ significantly from 3D rendering, the recently introduced hardware raytracing support in GPUs nonetheless provides an interesting new implementation option. In this article, we present a new room response simulation method, implemented in a vendor‐independent fashion with Vulkan compute shaders and leveraging NVIDIA VKRay hardware raytracing. We also extend this method to multi‐GPU computation with asynchronous streaming and introduce a domain‐specific high‐performance compression scheme in order to overcome the limitations of on‐board GPU memory and PCIe bandwidth when simulating very large scenes. Our implementation is, to the best of our knowledge, the first ever combined application of Vulkan hardware raytracing and multi‐GPU compute in a non‐rendering simulation setting. Compared to a state‐of‐the‐art multicore CPU implementation running on 12 CPU cores, we achieve an overall speedup factor of up to 20 on a single consumer GPU, and 71 on four GPUs.

GpuRIR: A python library for room impulse response simulation with GPU acceleration

The Image Source Method (ISM) is one of the most employed techniques to calculate acoustic Room Impulse Responses (RIRs), however, its computational complexity grows fast with the reverberation time of the room and its computation time can be prohibitive for some applications where a huge number of RIRs are needed. In this paper, we present a new implementation that dramatically improves the computation speed of the ISM by using Graphic Processing Units (GPUs) to parallelize both the simulation of multiple RIRs and the computation of the images inside each RIR. Additional speedups were achieved by exploiting the mixed precision capabilities of the newer GPUs and by using lookup tables. We provide a Python library under GNU license that can be easily used without any knowledge about GPU programming and we show that it is about 100 times faster than other state of the art CPU libraries. It may become a powerful tool for many applications that need to perform a large number of acoustic simulations, such as training machine learning systems for audio signal processing, or for real-time room acoustics simulations for immersive multimedia systems, such as augmented or virtual reality.

A generalized Fourier domain: Signal processing framework and applications

In this paper, a signal processing framework in a generalized Fourier domain (GFD) is introduced. In this newly proposed domain, a parametric form of control on the periodic repetitions that occur due to sampling in the reciprocal domain is possible, without the need to increase the sampling rate. This characteristic and the connections of the generalized Fourier transform to analyticity and to the z-transform are investigated. Core properties of the generalized discrete Fourier transform (GDFT) such as a weighted circular correlation property and Parseval's relation are derived. We show the benefits of using the novel framework in a spatial-audio application, specifically the simulation of room impulse responses for auralization purposes in e.g. virtual reality systems.

Rigid sphere room impulse response simulation: Algorithm and applications

Simulated room impulse responses have been proven to be both useful and indispensable for comprehensive testing of acoustic signal processing algorithms while controlling parameters such as the reverberation time, room dimensions, and source-array distance. In this work, a method is proposed for simulating the room impulse responses between a sound source and the microphones positioned on a spherical array. The method takes into account specular reflections of the source by employing the well-known image method, and scattering from the rigid sphere by employing spherical harmonic decomposition. Pseudocode for the proposed method is provided, taking into account various optimizations to reduce the computational complexity. The magnitude and phase errors that result from the finite order spherical harmonic decomposition are analyzed and general guidelines for the order selection are provided. Three examples are presented: an analysis of a diffuse reverberant sound field, a study of binaural cues in the presence of reverberation, and an illustration of the algorithm's use as a mouth simulator.

Optimal dispersion/dissipation-error finite-difference method for room impulse response simulation

The finite difference method is suitable for the calculation of acoustic impulse responses because of its computational simplicity and efficiency. However, for room acoustics applications, where reverberation makes it necessary to calculate the room impulse response and acoustic energy over a long duration, most standard finite-difference formulations are not practical because of the accumulation of dispersion and dissipation errors. In order to calculate very long impulse responses, a new, efficient, high-order finite-difference method is developed in this study. The new method is optimized to minimize the total dispersion and dissipation errors and hence the error accumulated in each time step resulting from discretization in both space and time. The error of the new method is significantly reduced so that the impulse response can be calculated over the whole reverberation time (about 1 to 3 s), using a relatively coarse spatial (6 grid points per wavelength) and a moderate temporal (CFL=1.0) resolutions. [Work supported by AFOSR.]