most citedCompression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training

11 citations · 13 across the 5 of their papers we have counts for

collaborators

5 papers

eess.AS2019

Compression of Acoustic Event Detection Models With Quantized Distillation

Bowen Shi, Ming Sun, Chieh-Chi Kao +3

Acoustic Event Detection (AED), aiming at detecting categories of events based on audio signals, has found application in many intelligent systems. Recently deep neural network sig…

cs.CL2019

Multimodal and Multi-view Models for Emotion Recognition

Gustavo Aguilar, Viktor Rozgić, Weiran Wang +1

Studies on emotion recognition (ER) show that combining lexical and acoustic information results in more robust and accurate models. The majority of the studies focus on settings w…

eess.AS201911 cited

Compression of Acoustic Event Detection Models with Low-rank Matrix Factorization and Quantization Training

Bowen Shi, Ming Sun, Chieh-Chi Kao +3

In this paper, we present a compression approach based on the combination of low-rank matrix factorization and quantization training, to reduce complexity for neural network based…

eess.AS2019

Semi-supervised Acoustic Event Detection based on tri-training

Bowen Shi, Ming Sun, Chieh-Chi Kao +3

This paper presents our work of training acoustic event detection (AED) models using unlabeled dataset. Recent acoustic event detectors are based on large-scale neural networks, wh…

cs.CV20172 cited

Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks

Zhuolin Jiang, Viktor Rozgic, Sancar Adali

Infrared (IR) imaging has the potential to enable more robust action recognition systems compared to visible spectrum cameras due to lower sensitivity to lighting conditions and ap…