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

Auditory selective attention plays a central role in the human capacity to reliably process complex sounds in multi-source environments. Stimulus reconstruction has been widely used for the investigation of selective auditory attention using multichannel electroencephalography (EEG). In particular, the influence of attention on sound representations in the brain has been modeled by linear time-variant filters and have been used to track the attentional state of individuals in multi-source environments.

Speech contains patterns that can be altered by the mood of an individual. There is an increasing focus on automated and distributed methods to collect and monitor speech from large groups of patients suffering from mental health disorders. However, as the scope of these collections increases, the variability in the data also increases. This variability is due in part to the range in the quality of the devices, which in turn affects the quality of the recorded data, negatively impacting the accuracy of automatic assessment.

Alternate direction method of multipliers (ADMM) technique has re- cently been proposed for LDPC decoding. Even though it improves the error rate performance compared with traditional message pass- ing (MP) techniques, it shows a higher computation complexity. In this article, the ADMM decoding algorithm is first described. Then, its computation complexity is analyzed. Finally, an optimized ver- sion which benefits from the multi-core processors architecture as well as the ADMM algorithm’s parallelism is presented.

In array processing, mutual coupling between sensors has an adverse effect on the estimation of parameters (e.g., DOA). Sparse arrays, such as nested arrays, coprime arrays, and minimum redundancy arrays (MRAs), have reduced mutual coupling compared to uniform linear arrays (ULAs). With $N$ denoting the number of sensors, these sparse arrays offer $O(N^2)$ freedoms for source estimation because their difference coarrays have $O(N^2)$-long ULA segments.

The movement of tongue plays an important role in pronunciation. Visualizing the movement of tongue can improve speech intelligibility and also helps learning a second language. However, hardly any research has been investigated for this topic. In this paper, a framework to synthesize continuous ultrasound tongue movement video from speech is presented. Two different mapping methods are introduced as the most important parts of the framework.

We tackle the problem of estimating sparse channels in massive MIMO-OFDM systems.
The code of our algorithms could be downloaded from Prof. Tareq Al-Naffouri's website http://faculty.kfupm.edu.sa/ee/naffouri/publications/demo_massiveMIMO.zip (460)
or from MATLAB File Exchange http://goo.gl/P19F1Y