Publications by authors named "Linjuan Cheng"

Traditional stereophonic acoustic echo cancellation algorithms need to estimate acoustic echo paths from stereo loudspeakers to a microphone, which often suffers from the nonuniqueness problem caused by a high correlation between the two far-end signals of these stereo loudspeakers. Many decorrelation methods have already been proposed to mitigate this problem. However, these methods may reduce the audio quality and/or stereophonic spatial perception.

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The state-of-the-art supervised binaural distance estimation methods often use binaural features that are related to both the distance and the azimuth, and thus the distance estimation accuracy may degrade a great deal with fluctuant azimuth. To incorporate the azimuth on estimating the distance, this paper proposes a supervised method to jointly estimate the azimuth and the distance of binaural signals based on deep neural networks (DNNs). In this method, the subband binaural features, including many statistical properties of several subband binaural features and the binaural spectral magnitude difference standard deviation, are extracted together as cues to jointly estimate the azimuth and the distance using binaural signals by exploiting a multi-objective DNN framework.

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The design of a new photocatalytic system and integrating the essential components in a structurally controlled manner to create artificially photosynthetic systems is high desirable. By incorporating a photoactive triphenylamine moiety to assemble a Gd-based metal-organic framework as a heterogeneous photosensitizer, new artificial systems were constructed for the proton and carbon dioxide reduction under irradiation. The assembled MOFs exhibited a one-dimensional metal-oxygen pillar that was connected together by the depronated TCA(3-) ligands to form a three-dimensional noninterpenetrating porous framework.

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