175 research outputs found
Toward the pre-cocktail party problem with TasTas
Deep neural network with dual-path bi-directional long short-term memory
(BiLSTM) block has been proved to be very effective in sequence modeling,
especially in speech separation, e.g. DPRNN-TasNet \cite{luo2019dual}, TasTas
\cite{shi2020speech}. In this paper, we propose two improvements of TasTas
\cite{shi2020speech} for end-to-end approach to monaural speech separation in
pre-cocktail party problems, which consists of 1) generate new training data
through the original training batch in real time, and 2) train each module in
TasTas separately. The new approach is called TasTas, which takes the mixed
utterance of five speakers and map it to five separated utterances, where each
utterance contains only one speaker's voice. For the objective, we train the
network by directly optimizing the utterance level scale-invariant
signal-to-distortion ratio (SI-SDR) in a permutation invariant training (PIT)
style. Our experiments on the public WSJ0-5mix data corpus results in 11.14dB
SDR improvement, which shows our proposed networks can lead to performance
improvement on the speaker separation task. We have open-sourced our
re-implementation of the DPRNN-TasNet in
https://github.com/ShiZiqiang/dual-path-RNNs-DPRNNs-based-speech-separation,
and our TasTas is realized based on this implementation of DPRNN-TasNet, it
is believed that the results in this paper can be reproduced with ease.Comment: arXiv admin note: substantial text overlap with arXiv:2001.08998,
arXiv:1902.04891, arXiv:1902.00651, arXiv:2008.0314
Robust Transceiver Design for MISO Interference Channel with Energy Harvesting
In this paper, we consider multiuser multiple-input single-output (MISO)
interference channel where the received signal is divided into two parts for
information decoding and energy harvesting (EH), respectively. The transmit
beamforming vectors and receive power splitting (PS) ratios are jointly
designed in order to minimize the total transmission power subject to both
signal-to-interference-plus-noise ratio (SINR) and EH constraints. Most joint
beamforming and power splitting (JBPS) designs assume that perfect channel
state information (CSI) is available; however CSI errors are inevitable in
practice. To overcome this limitation, we study the robust JBPS design problem
assuming a norm-bounded error (NBE) model for the CSI. Three different solution
approaches are proposed for the robust JBPS problem, each one leading to a
different computational algorithm. Firstly, an efficient semidefinite
relaxation (SDR)-based approach is presented to solve the highly non-convex
JBPS problem, where the latter can be formulated as a semidefinite programming
(SDP) problem. A rank-one recovery method is provided to recover a robust
feasible solution to the original problem. Secondly, based on second order cone
programming (SOCP) relaxation, we propose a low complexity approach with the
aid of a closed-form robust solution recovery method. Thirdly, a new iterative
method is also provided which can achieve near-optimal performance when the
SDR-based algorithm results in a higher-rank solution. We prove that this
iterative algorithm monotonically converges to a Karush-Kuhn-Tucker (KKT)
solution of the robust JBPS problem. Finally, simulation results are presented
to validate the robustness and efficiency of the proposed algorithms.Comment: 13 pages, 8 figures. arXiv admin note: text overlap with
arXiv:1407.0474 by other author
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