Sorry, you need to enable JavaScript to visit this website.

We address the problem of privately communicating audio messages to multiple listeners in a reverberant room using a set of loudspeakers. We propose two methods based on emitting noise. In the first method, the loudspeakers emit noise signals that are appropriately filtered so that after echoing along multiple paths in the room, they sum up and descramble to yield distinct meaningful audio messages only at specific focusing spots, while being incoherent everywhere else.

This paper proposes an approach to the joint modeling of the short-time Fourier transform magnitude and phase spectrograms with a deep generative model. We assume that the magnitude follows a Gaussian distribution and the phase follows a von Mises distribution. To improve the consistency of the phase values in the time-frequency domain, we also apply the von Mises distribution to the phase derivatives, i.e., the group delay and the instantaneous frequency. Based on these assumptions, we explore and compare several combinations of loss functions for training our models.

In this paper, we propose a new method for blind source separation, where we perform similarity search for a prepared clean speech database. The purpose of this mechanism is to separate short utterances that we frequently encounter in a real-world situation. The new method employs a local Gaussian model (LGM) for the probability density functions of separated signals, and updates the LGM variance parameters by using the similarity search results.

The objective of the study is to develop a framework for automatic breast cancer detection with merging four imaging modes. Attempts were made for tumor classification and segmentation; using a multi-parametric Magnetic Resonance Imaging (MRI) method on breast tumors. MRI data of the breast were obtained from 67 subjects with a 1.5T-MRI scanner. Four imaging modes: were T1 weighted, T2 weighted, Diffusion Weighted and eTHRIVE sequences, and dynamic- contrast-enhanced(DCE)-MRI parameters are acquired.

Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the “blind” environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR.

Free viewpoint video (FVV), owing to its comprehensive applications in immersive entertainment, remote surveillance and distanced education, has received extensive attention and been regarded as a new important direction of video technology development. Depth image-based rendering (DIBR) technologies are employed to synthesize FVV images in the “blind” environment. Therefore, a real-time reliable blind quality assessment metric is urgently required. However, existing stste-of-art quality assessment methods are limited to estimate geometric distortions generated by DIBR.

Point cloud segmentation is a key problem of 3D multimedia signal processing. Existing methods usually use single network structure which is trained by per-point loss. These methods mainly focus on geometric similarity between the prediction results and the ground truth, ignoring visual perception difference. In this paper, we present a segmentation adversarial network to overcome the drawbacks above. Discriminator is introduced to provide a perceptual loss to increase the rationality judgment of prediction and guide the further optimization of the segmentator.