Digital Signal Processing 2: Filtering 

2.1.1.a Linear time-invariant filters

2.1.1.b Convolution

2.1.2.a The moving average filter

2.1.2.b The leaky integrator

2.1.3.a Filter classification in the time domain

2.1.3.b Filter stability

2.1.4.a The convolution theorem

2.1.4.b Examples of frequency response

2.1.5.a Filter classification in the frequency domain

2.1.5.b The ideal lowpass filter

2.1.5.c Ideal filters derived from the ideal lowpass filter

2.1.5.d Demodulation revisited

SOTD: Can one hear the shape of a room?

Welcome to DSP Two!

Introduction

What have we learned?

Introduction

What have we learned?

Introduction

What have we learned?

Introduction

What have we learned?

Introduction

What have we learned?

Practice homework

Homework for Module 2.1

2.2.1.a Impulse truncation (and the Gibbs phenomenon)

2.2.1.b The window method

2.2.1.c Frequency sampling

2.2.2.a The z-transform

2.2.2.b Region of convergence and stability

2.2.3 Intuitive IIR designs

2.2.4.a Filter specifications

2.2.4.b IIR design

2.2.4.c FIR design

2.2.4.d Fractional delay and Hilbert filter

2.2.5.a Implementation of digital filters

2.2.5.b Real-time processing

Signal of the Day: Image Resolution and Space Exploration

Introduction

What have we learned?

Introduction

What have we learned?

Introduction

What have we learned?

Introduction

What have we learned?

Introduction

Practice homework

Notes and Supplementary Materials

Homework for Module 2.2

2.3.1.a Random Variables

2.3.1.b Stochastic Processes

2.3.1.c Power Spectral Density

2.3.1.d Filtering Random Processes

2.3.2.a Optimal Least Squares

2.3.2.b LPC Speech Coding

2.3.2.c The LMS Filter

2.3.2.d Echo Cancellation

Introduction

What have we learned?

Introduction

What have we learned?

Practice Homework

Notes and Supplementary Material

Homework for Module 2.3