Artificial enveloping reverberation for binaural auralization using reciprocal maximum-length sequences

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.

Perceptual roughness of spatially assigned sparse noise for rendering reverberation

Multichannel auralizations based on spatial room impulse responses often employ sample-wise assignment of an omnidirectional response to form loudspeaker responses. This leads to sparse impulse responses in each reproduction loudspeaker and the auralization of transient signals can sound rough. Based on this observation, we conducted a listening test to examine the general phenomenon of roughness due to spatial assignment. First, participants assessed the roughness of both Gaussian noise and velvet noise, assigned sample-wise to up to 36 loudspeakers by two algorithms. The first algorithm assigns channels merely by selecting random indices, while the second one constrains the time between two peaks on each channel. The results show that roughness already occurs when few channels are used and that the assignment algorithm influences it. In a second experiment, virtualizations of the test were used to examine the factors contributing to increased roughness. We systematically show the effect of spatial assignment on noise and conclude that besides time-differences, level-differences caused by head-shadowing are the principal cause for the perceived roughness. The results have significance in spatial room impulse response rendering and spatial reverberator design.

Perceptual analysis of directional late reverberation

The late reverberation characteristics of a sound field are often assumed to be perceptually isotropic, meaning that the decay of energy is perceived as equivalent in every direction. In this paper, we employ Ambisonics reproduction methods to reassess how a decaying sound field is analyzed and characterized and our capacity to hear directional characteristics within late reverberation. We propose the use of objective measures to assess the anisotropy characteristics of a decaying sound field. The energy-decay deviation is defined as the difference of the direction-dependent decay from the average decay. A perceptual study demonstrates a positive link between the range of these energy deviations and their audibility. These results suggest that accurate sound reproduction should account for directional properties throughout the decay.

Model-based Bayesian analysis in acoustics-A tutorial

Bayesian analysis has been increasingly applied in many acoustical applications. In these applications, prediction models are often involved to better understand the process under investigation by purposely learning from the experimental observations. When involving the model-based data analysis within a Bayesian framework, issues related to incorporating the experimental data and assigning probabilities into the inferential learning procedure need fundamental consideration. This paper introduces Bayesian probability theory on a tutorial level, including fundamental rules for manipulating the probabilities, and the principle of maximum entropy for assignment of necessary probabilities prior to the data analysis. This paper also employs a number of examples recently published in this journal to explain detailed steps on how to apply the model-based Bayesian inference to solving acoustical problems.

Introduction to the Special Issue on Room Acoustic Modeling and Auralization

This Special Issue on Room Acoustic Modeling and Auralization contains nineteen research papers. A majority of the papers focus on various room acoustic simulation techniques, while the remaining ones concentrate on auralization of either simulation or measurement results. Using room acoustic simulation, the last paper in this issue presents a case study of the historic venue, Palais du Trocadero in Paris, France.