564 citations · 572 across the 8 of their papers we have counts for
8 papers
Binaural Angular Separation Network
Yang Yang, George Sung, Shao-Fu Shih +3
We propose a neural network model that can separate target speech sources from interfering sources at different angular regions using two microphones. The model is trained with sim…
StreamVC: Real-Time Low-Latency Voice Conversion
Yang Yang, Yury Kartynnik, Yunpeng Li +4
We present StreamVC, a streaming voice conversion solution that preserves the content and prosody of any source speech while matching the voice timbre from any target speech. Unlik…
On-device Real-time Custom Hand Gesture Recognition
Esha Uboweja, David Tian, Qifei Wang +5
Most existing hand gesture recognition (HGR) systems are limited to a predefined set of gestures. However, users and developers often want to recognize new, unseen gestures. This i…
Learning to Detect Touches on Cluttered Tables
Norberto Adrian Goussies, Kenji Hata, Shruthi Prabhakara +28
We present a novel self-contained camera-projector tabletop system with a lamp form-factor that brings digital intelligence to our tables. We propose a real-time, on-device, learni…
Guided Speech Enhancement Network
Yang Yang, Shao-Fu Shih, Hakan Erdogan +5
High quality speech capture has been widely studied for both voice communication and human computer interface reasons. To improve the capture performance, we can often find multi-m…
Efficient Heterogeneous Video Segmentation at the Edge
Jamie Menjay Lin, Siargey Pisarchyk, Juhyun Lee +7
We introduce an efficient video segmentation system for resource-limited edge devices leveraging heterogeneous compute. Specifically, we design network models by searching across m…
On-device Real-time Hand Gesture Recognition
George Sung, Kanstantsin Sokal, Esha Uboweja +5
We present an on-device real-time hand gesture recognition (HGR) system, which detects a set of predefined static gestures from a single RGB camera. The system consists of two part…
MediaPipe Hands: On-device Real-time Hand Tracking
Fan Zhang, Valentin Bazarevsky, Andrey Vakunov +4
We present a real-time on-device hand tracking pipeline that predicts hand skeleton from single RGB camera for AR/VR applications. The pipeline consists of two models: 1) a palm de…