@inproceedings{fakoor2017reinforcement,
author = {Fakoor, Rasool and He, Xiaodong and Tashev, Ivan and Zarar, Shuayb},
title = {Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality},
booktitle = {Neural Information Processing Systems (NIPS)},
year = {2017},
month = {December},
abstract = {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. In this paper, we present a new approach to improve the adaptivity of such algorithms enabling them to perform robustly across a wide range of input signal and noise types. Our methodology is based on the dynamic control of algorithmic parameters via techniques of reinforcement learning. Specifically, we model the noise-suppression module as a black box, requiring no knowledge of the algorithmic mechanics except a simple feedback from the output. We utilize this feedback as the reward signal for a reinforcement learning agent that learns a policy to adapt the algorithmic parameters for every incoming audio frame (16 ms of data). Our preliminary results show that such a control mechanism can substantially increase the overall performance of the underlying noise-suppression algorithm; 42% and 16% improvements in output SNR and MSE, respectively, when compared to no adaptivity.},
url = {http://approjects.co.za/?big=en-us/research/publication/reinforcement-learning-adapt-speech-enhancement-instantaneous-input-signal-quality/},
note = {Wkshp. on Machine Learning for Audio Signal Processing},
}