54 citations · 234 across the 17 of their papers we have counts for
12 papers · 1 filter
MESH2IR: Neural Acoustic Impulse Response Generator for Complex 3D Scenes
Anton Ratnarajah, Zhenyu Tang, Rohith Chandrashekar Aralikatti +1
We propose a mesh-based neural network (MESH2IR) to generate acoustic impulse responses (IRs) for indoor 3D scenes represented using a mesh. The IRs are used to create a high-quali…
GWA: A Large High-Quality Acoustic Dataset for Audio Processing
Zhenyu Tang, Rohith Aralikatti, Anton Ratnarajah +1
We present the Geometric-Wave Acoustic (GWA) dataset, a large-scale audio dataset of about 2 million synthetic room impulse responses (IRs) and their corresponding detailed geometr…
FAST-RIR: Fast neural diffuse room impulse response generator
Anton Ratnarajah, Shi-Xiong Zhang, Meng Yu +3
We present a neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment. Our FAST-RI…
Point-based Acoustic Scattering for Interactive Sound Propagation via Surface Encoding
Hsien-Yu Meng, Zhenyu Tang, Dinesh Manocha
We present a novel geometric deep learning method to compute the acoustic scattering properties of geometric objects. Our learning algorithm uses a point cloud representation of ob…
TS-RIR: Translated synthetic room impulse responses for speech augmentation
Anton Ratnarajah, Zhenyu Tang, Dinesh Manocha
We present a method for improving the quality of synthetic room impulse responses for far-field speech recognition. We bridge the gap between the fidelity of synthetic room impulse…
IR-GAN: Room Impulse Response Generator for Far-field Speech Recognition
Anton Ratnarajah, Zhenyu Tang, Dinesh Manocha
We present a Generative Adversarial Network (GAN) based room impulse response generator (IR-GAN) for generating realistic synthetic room impulse responses (RIRs). IR-GAN extracts a…
Learning Acoustic Scattering Fields for Dynamic Interactive Sound Propagation
Zhenyu Tang, Hsien-Yu Meng, Dinesh Manocha
We present a novel hybrid sound propagation algorithm for interactive applications. Our approach is designed for dynamic scenes and uses a neural network-based learned scattered fi…
Scene-Aware Audio Rendering via Deep Acoustic Analysis
Zhenyu Tang, Nicholas J. Bryan, Dingzeyu Li +2
We present a new method to capture the acoustic characteristics of real-world rooms using commodity devices, and use the captured characteristics to generate similar sounding sourc…
Low-frequency Compensated Synthetic Impulse Responses for Improved Far-field Speech Recognition
Zhenyu Tang, Hsien-Yu Meng, Dinesh Manocha
We propose a method for generating low-frequency compensated synthetic impulse responses that improve the performance of far-field speech recognition systems trained on artificiall…
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation
Zhenyu Tang, Lianwu Chen, Bo Wu +2
We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-ba…
Regression and Classification for Direction-of-Arrival Estimation with Convolutional Recurrent Neural Networks
Zhenyu Tang, John D. Kanu, Kevin Hogan +1
We present a novel learning-based approach to estimate the direction-of-arrival (DOA) of a sound source using a convolutional recurrent neural network (CRNN) trained via regression…
Receiver Placement for Speech Enhancement using Sound Propagation Optimization
Nicolas Morales, Zhenyu Tang, Dinesh Manocha
A common problem in acoustic design is the placement of speakers or receivers for public address systems, telecommunications, and home smart speakers or digital personal assistants…