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ABET Course Objectives and Outcomes Form

Course number and title: EE114 Speech and Image Processing Systems Design
Credits: 4
Instructor(s)-in-charge: John Villasenor (villa@icsl.ucla.edu)
Course type: Lecture
Required or Elective: A pathway course.
Course Schedule:
Lecture: 3 hrs/week. Meets twice weekly.
Dicussion: 1 hr/discussion section. Multiple discussion sections offered per quarter.
Outside Study: 6 hrs/week.
Office Hours: 2 hrs/week by instructor. 2 hrs/week by each teaching assistant.
 
Course Assessment:
Exams: 1 midterm and 1 final examination.
 
Grading Policy: Typically, 10% design, 20% homework, 35% midterm, 35% final.
Course Prerequisites: EE113
Catalog Description: Design principles of speech and image processing systems. Speech production, analysis, and modeling in first half of course; design techniques for image enhancement, filtering, and transformation in second half. Lectures supplemented by laboratory implementation of speech and image processing tasks.  
Textbook and any related course material:
Course reader prepared by the instructors-in-charge.
 
Course Website
Topics covered in the course and level of coverage:
Review of transforms, linear and circular convolution; Up/Down sampling 2 hrs.
Sound generation and propagation in the vocal tract. 2 hrs.
Acoustic properties and parametric representations of speech signals. 2 hrs.
Short-time Fourier Transform (STFT) and applications. 4 hrs.
Speech Coding with a focus on Linear-Predictive Coding (LPC). 5 hrs.
Two dimensional signals, systems, and transforms. 6 hrs.
Image filtering and convolution. 5 hrs.
Edge detection. 2 hrs.
Image communications and compression. 2 hrs.
Examples of applications to speech and image processing. outside study
Course objectives and their relation to the Program Educational Objectives:  
Contribution of the course to the Professional Component:
Engineering Topics: 0 %
General Education: 0 %
Mathematics & Basic Sciences: 0 %
Expected level of proficiency from students entering the course:
Mathematics: Strong
Physics: Not Applicable
Chemistry: Not Applicable
Technical writing: Some
Computer Programming: Some
Material available to students and department at end of course:
  Available to
students
Available to
department
Available to
instructor
Available to
TA(s)
Course Objectives and Outcomes Form: X X X X
Lecture notes, homework assignments, and solutions: X
Samples of homework solutions from 2 students: X
Samples of exam solutions from 2 students: X
Course performance form from student surveys: X X
Will this course involve computer assignments? NO Will this course have TA(s) when it is offered? YES

  Level of contribution of course to Program Outcomes
(a) Strong  
(b) Strong  
(c) Strong  
(e) Strong  
(i) Strong  
(j) Some  
(k) Average  
(m) Some  
Strong: (a) (b) (c) (e) (i)
Average: (k)
Some: (j) (m)

:: Upon completion of this course, students will have had an opportunity to learn about the following ::
  Specific Course Outcomes Program Outcomes
1. Understand how speech is produced. a e j k m
2. Understand and know acoustic properties of various speech sounds. a b e j k m
3. Perform the Short-Time Fourier Transform (STFT). a e j k m
4. Understand the basic tradeoffs when performing the STFT in a computer laboratory setting. a b e j k m
5. Understand basic concepts of speech coding. a e j k m
6. Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory. a b e j k m
7. Perform forward and inverse two dimensional Fourier transforms. a b e k m
8. Perform two dimensional convolution. a b e k m
9. Design two dimensional convolution masks to achieve a desired effect on an image. a b c e k m
10. Understand and analyze the meaning of two dimensional frequency and its relation to image characteristics. a b e k m
11. Understand and analyze alternative (non Fourier) unitary transforms such as the DCT. a b e k m
12. Understand and analyze image communications systems. a b e k m
13. Several homework assignments delving on core concepts and reinforcing analytical skills learned in class. a b e k m
14. Several computer assignments exposing students to typical applications of digital signal processing and asking them to carry out simple yet illustrative design projects. a b e k m
15. Opportunities to interact weekly with the instructor and the teaching assistant(s) during regular office hours and discussion sections in order to further their learning experience and their interest in the material. a i

  Program outcomes and how they are covered by the specific course outcomes
(a)   Understand how speech is produced.  
  Understand and know acoustic properties of various speech sounds.  
  Perform the Short-Time Fourier Transform (STFT).  
  Understand the basic tradeoffs when performing the STFT in a computer laboratory setting.  
  Understand basic concepts of speech coding.  
  Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory.  
  Perform forward and inverse two dimensional Fourier transforms.  
  Perform two dimensional convolution.  
  Design two dimensional convolution masks to achieve a desired effect on an image.  
  Understand and analyze the meaning of two dimensional frequency and its relation to image characteristics.  
  Understand and analyze alternative (non Fourier) unitary transforms such as the DCT.  
  Understand and analyze image communications systems.  
  Several homework assignments delving on core concepts and reinforcing analytical skills learned in class.  
  Several computer assignments exposing students to typical applications of digital signal processing and asking them to carry out simple yet illustrative design projects.  
  Opportunities to interact weekly with the instructor and the teaching assistant(s) during regular office hours and discussion sections in order to further their learning experience and their interest in the material.  
(b)   Understand and know acoustic properties of various speech sounds.  
  Understand the basic tradeoffs when performing the STFT in a computer laboratory setting.  
  Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory.  
  Perform forward and inverse two dimensional Fourier transforms.  
  Perform two dimensional convolution.  
  Design two dimensional convolution masks to achieve a desired effect on an image.  
  Understand and analyze the meaning of two dimensional frequency and its relation to image characteristics.  
  Understand and analyze alternative (non Fourier) unitary transforms such as the DCT.  
  Understand and analyze image communications systems.  
  Several homework assignments delving on core concepts and reinforcing analytical skills learned in class.  
  Several computer assignments exposing students to typical applications of digital signal processing and asking them to carry out simple yet illustrative design projects.  
(c)   Design two dimensional convolution masks to achieve a desired effect on an image.  
(e)   Understand how speech is produced.  
  Understand and know acoustic properties of various speech sounds.  
  Perform the Short-Time Fourier Transform (STFT).  
  Understand the basic tradeoffs when performing the STFT in a computer laboratory setting.  
  Understand basic concepts of speech coding.  
  Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory.  
  Perform forward and inverse two dimensional Fourier transforms.  
  Perform two dimensional convolution.  
  Design two dimensional convolution masks to achieve a desired effect on an image.  
  Understand and analyze the meaning of two dimensional frequency and its relation to image characteristics.  
  Understand and analyze alternative (non Fourier) unitary transforms such as the DCT.  
  Understand and analyze image communications systems.  
  Several homework assignments delving on core concepts and reinforcing analytical skills learned in class.  
  Several computer assignments exposing students to typical applications of digital signal processing and asking them to carry out simple yet illustrative design projects.  
(i)   Opportunities to interact weekly with the instructor and the teaching assistant(s) during regular office hours and discussion sections in order to further their learning experience and their interest in the material.  
(j)   Understand how speech is produced.  
  Understand and know acoustic properties of various speech sounds.  
  Perform the Short-Time Fourier Transform (STFT).  
  Understand the basic tradeoffs when performing the STFT in a computer laboratory setting.  
  Understand basic concepts of speech coding.  
  Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory.  
(k)   Understand how speech is produced.  
  Understand and know acoustic properties of various speech sounds.  
  Perform the Short-Time Fourier Transform (STFT).  
  Understand the basic tradeoffs when performing the STFT in a computer laboratory setting.  
  Understand basic concepts of speech coding.  
  Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory.  
  Perform forward and inverse two dimensional Fourier transforms.  
  Perform two dimensional convolution.  
  Design two dimensional convolution masks to achieve a desired effect on an image.  
  Understand and analyze the meaning of two dimensional frequency and its relation to image characteristics.  
  Understand and analyze alternative (non Fourier) unitary transforms such as the DCT.  
  Understand and analyze image communications systems.  
  Several homework assignments delving on core concepts and reinforcing analytical skills learned in class.  
  Several computer assignments exposing students to typical applications of digital signal processing and asking them to carry out simple yet illustrative design projects.  
(m)   Understand how speech is produced.  
  Understand and know acoustic properties of various speech sounds.  
  Perform the Short-Time Fourier Transform (STFT).  
  Understand the basic tradeoffs when performing the STFT in a computer laboratory setting.  
  Understand basic concepts of speech coding.  
  Perform Linear-Predictive Coding (LPC) on speech signals theoretically and in a computer laboratory.  
  Perform forward and inverse two dimensional Fourier transforms.  
  Perform two dimensional convolution.  
  Design two dimensional convolution masks to achieve a desired effect on an image.  
  Understand and analyze the meaning of two dimensional frequency and its relation to image characteristics.  
  Understand and analyze alternative (non Fourier) unitary transforms such as the DCT.  
  Understand and analyze image communications systems.  
  Several homework assignments delving on core concepts and reinforcing analytical skills learned in class.  
  Several computer assignments exposing students to typical applications of digital signal processing and asking them to carry out simple yet illustrative design projects.  

:: Last modified: February 2013 by J. Lin ::

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