ECE 417: Multimedia Signal Processing
ECE 417: Multimedia Signal Processing
ECE 417 Multimedia Signal Processing. Characteristics of speech and image signals; important analysis and
synthesis tools for multimedia signal processing including subspace
methods, Bayesian networks, hidden Markov models, and factor graphs;
applications to biometrics (person identification), human-computer
interaction (face and gesture recognition and synthesis), and
audio-visual databases (indexing and retrieval). Emphasis on a set of
python machine problems providing hands-on experience. 4 undergraduate
hours. 4 graduate hours. Prerequisite: ECE 310 and ECE 313.
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When you window a speech signal, it's possible to describe the resulting DTFT very precisely. This lecture gives you exactly the formulas for the DTFT of a…
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Basic information about speech signals: voiced and unvoiced excitation models, relation between articulation and acoustics, power spectrum.
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Quick overview of random signals: the expected value of the power spectrum and the autocorrelation, Wiener's theorem, white noise.
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Review of convolution in time and convolution in frequency. Review of the Fourier transform of a rectangle function, and the inverse Fourier transform of an ideal…
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Review of the four basic Fourier transforms, their Parseval's theorems, and the way in which they transform impulses and pure tones.
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Zoom Recording ID: 95558032096 UUID: sxLhERomShCMsETftuU9uA== Meeting Time: 2020-12-08T15:28:22Z
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