Project 1: LPC Lossy Compression
of Speech Signals
Omar Shanta (A09810559)
Professor: Ken Kreutz-Delgado
ECE 174
11/01/2016
2
Objective/Purpose/Introduction
Things to include:
State the Problem
Why is this problem important?
What is the overall goal/go
ECE 174
Intro to Linear &
Nonlinear Optimization
Ken Kreutz-Delgado
ECE Department UCSD
Contact Information Fall 2016
Course Website
Accessible from http:/dsp.ucsd.edu/~kreutz
Course Lecture Time & Location
TuTh 3:30-4:50pm, Pepper Canyon Hall, Room 106
Vector Space Concepts
ECE 174 Introduction to Linear & Nonlinear Optimization
Ken Kreutz-Delgado
ECE Department, UC San Diego
Ken Kreutz-Delgado (UC San Diego)
ECE 174
Fall 2016
1 / 25
Need for Vector Space Theory
What are Vectors and Linear Vector Spaces
Normed & Inner Product Vector Spaces
ECE 174 Introduction to Linear & Nonlinear Optimization
Ken Kreutz-Delgado
ECE Department, UC San Diego
Ken Kreutz-Delgado (UC San Diego)
ECE 174
Fall 2016
1 / 27
Normed Linear Vector Space
In a vector space it is usef
Review Material
Chapters 1-3 of Matrix Analyis
Textbook
Textbook
Example 1.2.1
Textbook
Example 1.3.1
Textbook
Example 2.1.1
Textbook
Example 2.1.2
Textbook
Example 2.2.2
If such
relationships
exist, then the
columns
are said to be
"linearly
dependent."
O
ECE 174 Homework # 2 Due Tuesday 10/18/2016
READING
Immediately begin reading Sections 4.1-4.5. Particularly ponder the Summary of Rank chart on page
218. You can ignore examples 4.3.6, 4.4.6, 4.4.7, 4.5.2, and 4.5.3. (Later, but not now, we will read 4.6
ECE 174 Homework # 1 Due Thursday 9/29/2016
Assumed Background
Students are assumed to have had a prior course in Linear Algebra, and therefore to already
know relevant material presented in Chapters 1-3 of the assigned textbook (see the reading
requireme
ECE 174 Homework # 3 Due Thursday, 11/3/2016
There are ten (10) questions on this homework assignment. Remember, the Solutions
Manual is provided with the text. Errata for the textbook are available at the website:
http:/MatrixAnalysis.com
Reading From Ch
ECE 174 Computer Assignment #1
LEAST SQUARES AUDIO AND SPEECH COMPRESSION
LINEAR PREDICTIVE CODING (LPC)
Background on LPC Lossy Compression of Speech Signals
Speech, and other audio, signals represented by sample data Y N = cfw_y(n), n = 1, 2, , N ,
are