54 citations · 234 across the 17 of their papers we have counts for
18 papers
RH20T: A Comprehensive Robotic Dataset for Learning Diverse Skills in One-Shot
Hao-Shu Fang, Hongjie Fang, Zhenyu Tang +5
A key challenge in robotic manipulation in open domains is how to acquire diverse and generalizable skills for robots. Recent research in one-shot imitation learning has shown prom…
Exploring Data Redundancy in Real-world Image Classification through Data Selection
Zhenyu Tang, Shaoting Zhang, Xiaosong Wang
Deep learning models often require large amounts of data for training, leading to increased costs. It is particularly challenging in medical imaging, i.e., gathering distributed da…
Synthetic Wave-Geometric Impulse Responses for Improved Speech Dereverberation
Rohith Aralikatti, Zhenyu Tang, Dinesh Manocha
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-fr…
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…
Online Self-Attentive Gated RNNs for Real-Time Speaker Separation
Ori Kabeli, Yossi Adi, Zhenyu Tang +2
Deep neural networks have recently shown great success in the task of blind source separation, both under monaural and binaural settings. Although these methods were shown to produ…
Improving Reverberant Speech Separation with Multi-stage Training and Curriculum Learning
Rohith Aralikatti, Anton Ratnarajah, Zhenyu Tang +1
We present a novel approach that improves the performance of reverberant speech separation. Our approach is based on an accurate geometric acoustic simulator (GAS) which generates…
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…
Scene-aware Far-field Automatic Speech Recognition
Zhenyu Tang, Dinesh Manocha
We propose a novel method for generating scene-aware training data for far-field automatic speech recognition. We use a deep learning-based estimator to non-intrusively compute the…
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…