ojusd / ece_417-multimedia_signal_processing

Contains MPs(Programming Assignments) 1-7 for ECE 417 at UIUC

ECE_417_MPs

Contains MPs(Programming Assignments) 1-7 for ECE 417 at UIUC.

ECE 417 at UIUC covers: 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 MATLAB machine problems providing hands-on experience.

A. After Machine Problem 1 (MP1), Week 3 of the semester, the students should be able to:

• Understand speech features (esp. cepstrum coefficients) and nearest-neighbor pattern classifies and their applications to speech recognition and speaker identification (a,l)

B. After MP2, Week 5 of the semester, the students should be able to:

• Understand principal component analysis and linear discriminant analysis, and their applications to face recognition (a,l)

C. After MP3, Week 7 of the semester, the students should be able to:

• Understand maximum likelihood (ML) classifies, Bayesian networks, and multimodal fusion, and their applications to audio-visual person identification (a,l)

D. After MP4, Week 9 of the semester, the students should be able to:

• Understand hidden Markov model (HMM), including algorithms for learning, inference, and decoding, and its application to audio-visual speech recognition (a,l)

E. After MP5, Week 11 of the semester, the students should be able to:

• Understand 3D face modeling and animation and applications to speech-driven lip movement in an audio-visual avatar (synthetic talking head) (a,l)

F. After MP6, Week 13 of the semester, the students should be able to:

• Understand and practice content-based image retrieval with relevance feedback (a,l)

G. After MP7, Week 15 of the semester, the students should be able to:

• Understand structuring and indexing video data, and algorithms for video shot segmentation based on audio and visual cues (a,l)