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Learning Bimodal Structure in Audio–Visual Data - IEEE ...
https://ieeexplore.ieee.org/document/5280184#:~:text=Learning%20Bimodal%20Structure%20in%20Audio%E2%80%93Visual%20Data%20Abstract%3AA%20novel,represented%20as%20a%20sparse%20sum%20of%20audio-visual%20kernels.
Learning bimodal structure in audio-visual data
https://pubmed.ncbi.nlm.nih.gov/19963447/
A novel model is presented to learn bimodally informative structures from audio-visual signals. The signal is represented as a sparse sum of audio-visual kernels. Each kernel is a bimodal function consisting of synchronous snippets of an audio waveform and a spatio-temporal visual basis function. To represent an audio-visual signal, the kernels can be positioned …
(PDF) Learning Bimodal Structure in Audio-Visual Data
https://www.researchgate.net/publication/40450421_Learning_Bimodal_Structure_in_Audio-Visual_Data
The basis functions that emerge during learning capture salient audio-visual data structures. In addition, it is demonstrated that the learned dictionary can be used to …
CiteSeerX — Learning Bimodal Structure in Audio-Visual …
https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.167.1901
The proposed algorithm uses unsupervised learning to form dictionaries of bimodal kernels from audio-visual material. The basis functions that emerge during learning capture salient audio-visual data structures. In addition it is demonstrated that the learned dictionary can be used to locate sources of sound in the movie frame. Specifically, in sequences containing two …
Learning Bimodal Structure in Audio–Visual Data - IEEE ...
https://ieeexplore.ieee.org/document/5280184
The proposed algorithm uses unsupervised learning to form dictionaries of bimodal kernels from audio-visual material. The basis functions that emerge during learning capture salient audio-visual data structures. In addition, it is demonstrated that the learned dictionary can be used to locate sources of sound in the movie frame. Specifically, in …
Review: Learning Bimodal Structures in Audio-Visual Data
http://www.cse.buffalo.edu/~jcorso/t/2014S_SEM/files/suren_MonaciTNN2009.pdf
Audio visual data s = (a;v) , a(t), v(x;y;t) Dictionary f˚ kg, ˚ k = (˚ (a) k (t);˚ (v) k (x;y;t)) Each atom can be translated to any point in space and time using operator T (p;q;r) T (p;q;r) = (˚ (a) k (t r);˚ (v) k (x p;y q;t r)) Thus an audio-visual signal can be represented as s ˇ P K k=1 P n k i=1 c k i T (p ;q r) k i ˚ k, c k i = (c(a) k;c (v) k)
Learning Bimodal Structure in Audio-Visual Data
https://core.ac.uk/download/pdf/147941825.pdf
Learning Bimodal Structure in Audio-Visual Data Gianluca Monaci, Pierre Vandergheynst and Friedrich T. Sommer AbstractŠA novel model is presented to learn bimodally informative structures from audio-visual signals. The signal is represented as a sparse sum of audio-visual kernels. Each kernel is a bimodal function consisting of synchronous snippets of an
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