2 citations · 3 across the 4 of their papers we have counts for
8 papers
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…
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…