TMM Volume 22 Issue 3

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March, 2020

TMM Volume 22 Issue 3

This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing systems. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible.

Research in light field (LF) processing has heavily increased over the last decade. This is largely driven by the desire to achieve the same level of immersion and navigational freedom for camera-captured scenes as it is currently available for CGI content. Standardization organizations such as MPEG and JPEG continue to follow conventional coding paradigms in which viewpoints are discretely represented on 2-D regular grids.

Acoustic event detection deals with the acoustic signals to determine the sound type and to estimate the audio event boundaries. Multi-label classification based approaches are commonly used to detect the frame wise event types with a median filter applied to determine the happening acoustic events. However, the multi-label classifiers are trained only on the acoustic event types ignoring the frame position within the audio events.

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