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Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality
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 instantaneous input signal-to-noise ratios.
Constrained Convolutional-Recurrent Networks to Improve Speech Quality with Low Impact on Recognition Accuracy
For a speech-enhancement algorithm, it is highly desirable to simultaneously improve perceptual quality and recognition rate.
Convolutional-Recurrent Neural Networks for Speech Enhancement
By incorporating prior knowledge of speech signals into the design of model structures, we build a model that is more data-efficient and achieves better generalization on both seen and unseen noise.
Towards efficient models for real-time deep noise suppression
It is shown that the achievable speech quality is a function of network complexity, and show which models have better tradeoffs.
On training targets for noise-robust voice activity detection
The task of voice activity detection (VAD) is an often required module in various speech processing, analysis and classification tasks.
Low complexity online convolutional beamforming
Convolutional beamformers integrate the multichannel linear prediction model into beamformers, which provide good performance and optimality for joint dereverberation and noise reduction tasks.
