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Found 9 papers, 1 papers with code
Mean Field Game of High-Frequency Anticipatory Trading
The interactions between a large population of high-frequency traders (HFTs) and a large trader (LT) who executes a certain amount of assets at discrete time points are studied.
Trading Large Orders in the Presence of Multiple High-Frequency Anticipatory Traders
We investigate a market with a normal-speed informed trader (IT) who may employ mixed strategy and multiple anticipatory high-frequency traders (HFTs) who are under different inventory pressures, in a three-period Kyle's model.
Employing Real Training Data for Deep Noise Suppression
It provides a reference-free perceptual loss for employing real data during DNS training, maximizing the PESQ scores.
The Effects of High-frequency Anticipatory Trading: Small Informed Trader vs. Round-Tripper
In an extended Kyle's model, the interactions between a large informed trader and a high-frequency trader (HFT) who can anticipate the former's incoming order are studied.
Coded Speech Quality Measurement by a Non-Intrusive PESQ-DNN
We illustrate the potential of this model by predicting the PESQ scores of wideband-coded speech obtained from AMR-WB or EVS codecs operating at different bitrates in noisy, tandeming, and error-prone transmission conditions.
Are Large Traders Harmed by Front-running HFTs?
This paper studies the influences of a high-frequency trader (HFT) on a large trader whose future trading is predicted by the former.
Does a PESQNet (Loss) Require a Clean Reference Input? The Original PESQ Does, But ACR Listening Tests Don't
Detailed analyses show that the DNS trained with the MF-intrusive PESQNet outperforms the Interspeech 2021 DNS Challenge baseline and the same DNS trained with an MSE loss by 0. 23 and 0. 12 PESQ points, respectively.
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet
Perceptual evaluation of speech quality (PESQ) is a widely used metric for evaluating speech quality.
Deep Noise Suppression With Non-Intrusive PESQNet Supervision Enabling the Use of Real Training Data
During the training process, most of the speech enhancement neural networks are trained in a fully supervised way with losses requiring noisy speech to be synthesized by clean speech and additive noise.
