arXiv subjects

Teng Xiang

Publications and source records attributed to Teng Xiang.

3 recordsLinked to original sources

arXiv2023

Sensitivity study of the charged lepton flavor violating process $\tau \to \gamma \mu$ at STCF

A sensitivity study for the search for the charged lepton flavor violating process $\tau \to \gamma\mu$ at the Super $\tau$-Charm Facility is performed with a fast simulation. With the expected performance of the current detector design and an integrated luminosity of \SI{1}{ab^{-1}} corresponding to one-year of data taking, the sensitivity on the branching fraction (BF) of $\tau \to \gamma\mu$ is estimated to be at the level of \num{e-8}. The sensitivity under different detector performances are also studied. With ideal performance, the BF could be probed to be \num{2.8e-8} at \SI{90}{\percent} confidence level. The sensitivity is expected to scale with the square root of the luminosity, therefore with a total luminosity of \SI{10}{ab^{-1}} corresponding to ten-year of data taking, the sensitivity could reach \num{8.8e-9}, which is about one order of magnitude improvement upon the current best upper limit.

hep-ex↗

arXiv2020

Nonlinear Residual Echo Suppression Based on Multi-stream Conv-TasNet

Acoustic echo cannot be entirely removed by linear adaptive filters due to the nonlinear relationship between the echo and far-end signal. Usually a post processing module is required to further suppress the echo. In this paper, we propose a residual echo suppression method based on the modification of fully convolutional time-domain audio separation network (Conv-TasNet). Both the residual signal of the linear acoustic echo cancellation system, and the output of the adaptive filter are adopted to form multiple streams for the Conv-TasNet, resulting in more effective echo suppression while keeping a lower latency of the whole system. Simulation results validate the efficacy of the proposed method in both single-talk and double-talk situations.

eess.AS↗

arXiv2018

RLS-Based Adaptive Dereverberation Tracing Abrupt Position Change of Target Speaker

Adaptive algorithm based on multi-channel linear prediction is an effective dereverberation method balancing well between the attenuation of the long-term reverberation and the dereverberated speech quality. However, the abrupt change of the speech source position, usually caused by the shift of the speakers, forms an obstacle to the adaptive algorithm and makes it difficult to guarantee both the fast convergence speed and the optimal steady-state behavior. In this paper, the RLS-based adaptive multi-channel linear prediction method is investigated and a time-varying forgetting factor based on the relative weighted change of the adaptive filter coefficients is proposed to effectively tracing the abrupt change of the target speaker position. The advantages of the proposed scheme are demonstrated in the simulations and experiments.

eess.AS↗