4,951 research outputs found
GWA: A Large High-Quality Acoustic Dataset for Audio Processing
We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio
dataset of over 2 million synthetic room impulse responses (IRs) and their
corresponding detailed geometric and simulation configurations. Our dataset
samples acoustic environments from over 6.8K high-quality diverse and
professionally designed houses represented as semantically labeled 3D meshes.
We also present a novel real-world acoustic materials assignment scheme based
on semantic matching that uses a sentence transformer model. We compute
high-quality impulse responses corresponding to accurate low-frequency and
high-frequency wave effects by automatically calibrating geometric acoustic
ray-tracing with a finite-difference time-domain wave solver. We demonstrate
the higher accuracy of our IRs by comparing with recorded IRs from complex
real-world environments. The code and the full dataset will be released at the
time of publication. Moreover, we highlight the benefits of GWA on audio deep
learning tasks such as automated speech recognition, speech enhancement, and
speech separation. We observe significant improvement over prior synthetic IR
datasets in all tasks due to using our dataset.Comment: Project webpage https://gamma.umd.edu/pro/sound/gw
Broadband DOA estimation using Convolutional neural networks trained with noise signals
A convolution neural network (CNN) based classification method for broadband
DOA estimation is proposed, where the phase component of the short-time Fourier
transform coefficients of the received microphone signals are directly fed into
the CNN and the features required for DOA estimation are learnt during
training. Since only the phase component of the input is used, the CNN can be
trained with synthesized noise signals, thereby making the preparation of the
training data set easier compared to using speech signals. Through experimental
evaluation, the ability of the proposed noise trained CNN framework to
generalize to speech sources is demonstrated. In addition, the robustness of
the system to noise, small perturbations in microphone positions, as well as
its ability to adapt to different acoustic conditions is investigated using
experiments with simulated and real data.Comment: Published in Proceedings of IEEE Workshop on Applications of Signal
Processing to Audio and Acoustics (WASPAA) 201
Spatial Sound Rendering – A Survey
Simulating propagation of sound and audio rendering can improve the sense of realism and the immersion both in complex acoustic environments and dynamic virtual scenes. In studies of sound auralization, the focus has always been on room acoustics modeling, but most of the same methods are also applicable in the construction of virtual environments such as those developed to facilitate computer gaming, cognitive research, and simulated training scenarios. This paper is a review of state-of-the-art techniques that are based on acoustic principles that apply not only to real rooms but also in 3D virtual environments. The paper also highlights the need to expand the field of immersive sound in a web based browsing environment, because, despite the interest and many benefits, few developments seem to have taken place within this context. Moreover, the paper includes a list of the most effective algorithms used for modelling spatial sound propagation and reports their advantages and disadvantages. Finally, the paper emphasizes in the evaluation of these proposed works
Recommended from our members
Efficient Acoustic Simulation for Immersive Media and Digital Fabrication
Sound is a crucial part of our life. Well-designed acoustic behaviors can lead to significant improvement in both physical and virtual interactions. In computer graphics, most existing methods focused primarily on improving the accuracy. It remained underexplored on how to develop efficient acoustic simulation algorithms for interactive practical applications.
The challenges arise from the dilemma between expensive accurate simulations and fast feedback demanded by intuitive user interaction: traditional physics-based acoustic simulations are computationally expensive; yet, for end users to benefit from the simulations, it is crucial to give prompt feedback during interactions.
In this thesis, I investigate how to develop efficient acoustic simulations for real-world applications such as immersive media and digital fabrication. To address the above-mentioned challenges, I leverage precomputation and optimization to significantly improve the speed while preserving the accuracy of complex acoustic phenomena. This work discusses three efforts along this research direction: First, to ease sound designer's workflow, we developed a fast keypoint-based precomputation algorithm to enable interactive acoustic transfer values in virtual sound simulations. Second, for realistic audio editing in 360° videos, we proposed an inverse material optimization based on fast sound simulation and a hybrid ambisonic audio synthesis that exploits the directional isotropy in spatial audios. Third, we devised a modular approach to efficiently simulate and optimize fabrication-ready acoustic filters, achieving orders of magnitudes speedup while maintaining the simulation accuracy. Through this series of projects, I demonstrate a wide range of applications made possible by efficient acoustic simulations
Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation
We present a novel approach to improve the performance of learning-based
speech dereverberation using accurate synthetic datasets. Our approach is
designed to recover the reverb-free signal from a reverberant speech signal. We
show that accurately simulating the low-frequency components of Room Impulse
Responses (RIRs) is important to achieving good dereverberation. We use the GWA
dataset that consists of synthetic RIRs generated in a hybrid fashion: an
accurate wave-based solver is used to simulate the lower frequencies and
geometric ray tracing methods simulate the higher frequencies. We demonstrate
that speech dereverberation models trained on hybrid synthetic RIRs outperform
models trained on RIRs generated by prior geometric ray tracing methods on four
real-world RIR datasets.Comment: Submitted to ICASSP 202
Analysis, modeling and wide-area spatiotemporal control of low-frequency sound reproduction
This research aims to develop a low-frequency response control methodology capable of delivering a consistent spectral and temporal response over a wide listening area. Low-frequency room acoustics are naturally plagued by room-modes, a result of standing waves at frequencies with wavelengths that are integer multiples of one or more room dimension. The standing wave pattern is different for each modal frequency, causing a complicated sound field exhibiting a highly position-dependent frequency response. Enhanced systems are investigated with multiple degrees of freedom (independently-controllable sound radiating sources) to provide adequate low-frequency response control. The proposed solution, termed a chameleon subwoofer array or CSA, adopts the most advantageous aspects of existing room-mode correction methodologies while emphasizing efficiency and practicality. Multiple degrees of freedom are ideally achieved by employing what is designated a hybrid subwoofer, which provides four orthogonal degrees of freedom configured within a modest-sized enclosure. The CSA software algorithm integrates both objective and subjective measures to address listener preferences including the possibility of individual real-time control. CSAs and existing techniques are evaluated within a novel acoustical modeling system (FDTD simulation toolbox) developed to meet the requirements of this research. Extensive virtual development of CSAs has led to experimentation using a prototype hybrid subwoofer. The resulting performance is in line with the simulations, whereby variance across a wide listening area is reduced by over 50% with only four degrees of freedom. A supplemental novel correction algorithm addresses correction issues at select narrow frequency bands. These frequencies are filtered from the signal and replaced using virtual bass to maintain all aural information, a psychoacoustical effect giving the impression of low-frequency. Virtual bass is synthesized using an original hybrid approach combining two mainstream synthesis procedures while suppressing each method‟s inherent weaknesses. This algorithm is demonstrated to improve CSA output efficiency while maintaining acceptable subjective performance
A round robin on room acoustical simulation and auralization
A round robin was conducted to evaluate the state of the art of room acoustic modeling software both in the physical and perceptual realms. The test was based on six acoustic scenes highlighting specific acoustic phenomena and for three complex, “real-world” spatial environments. The results demonstrate that most present simulation algorithms generate obvious model errors once the assumptions of geometrical acoustics are no longer met. As a consequence, they are neither able to provide a reliable pattern of early reflections nor do they provide a reliable prediction of room acoustic parameters outside a medium frequency range. In the perceptual domain, the algorithms under test could generate mostly plausible but not authentic auralizations, i.e., the difference between simulated and measured impulse responses of the same scene was always clearly audible. Most relevant for this perceptual difference are deviations in tone color and source position between measurement and simulation, which to a large extent can be traced back to the simplified use of random incidence absorption and scattering coefficients and shortcomings in the simulation of early reflections due to the missing or insufficient modeling of diffraction.DFG, 174776315, FOR 1557: Simulation and Evaluation of Acoustical Environments (SEACEN
- …