13 citations · 15 across the 3 of their papers we have counts for
4 papers
Convolutional-Recurrent Neural Networks for Speech Enhancement
Han Zhao, Shuayb Zarar, Ivan Tashev +1
We propose an end-to-end model based on convolutional and recurrent neural networks for speech enhancement. Our model is purely data-driven and does not make any assumptions about…
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy
Rasool Fakoor, Xiaodong He, Ivan Tashev +1
For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate. Thanks to computational costs and model complexities,…
Precision Scaling of Neural Networks for Efficient Audio Processing
Jong Hwan Ko, Josh Fromm, Matthai Philipose +2
While deep neural networks have shown powerful performance in many audio applications, their large computation and memory demand has been a challenge for real-time processing. In t…
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality
Rasool Fakoor, Xiaodong He, Ivan Tashev +1
Today, the optimal performance of existing noise-suppression algorithms, both data-driven and those based on classic statistical methods, is range bound to specific levels of insta…