Signal, Audio and Image Processing
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About this ebook
Part I includes design and implementation of different types of filters for filtering signal that has different sinusoidal frequency components or noise. The comparison was made between FIR low pass flter, butterworth filter, Chebycheve Type I low pass filter and Chebycheve Type II low pass filter. Then different types of IIR Butterworth filters were designed and implemented to filter a signal that has many harmonics components, including low pass filter, high pass filter, stop band filter and band pass filter.
Part II examined audio filtering in the sense of specific frequency suppression and extraction. There are many different types of filters available for the construction of filters. We will specifically use the Butterworth filter. An audio signal was read and different types of filters, including low pass filter, high pass filter, stop band filter and band pass filter, were designed and implemented in order to filter the audio signal from some frequency bands. Then the discrete cosine transform compression examined on the audio signal at different compression rates: 50%, 75% , 87.5%
Part III deals with image processing; the project shows examples in smoothing, sharpening, and edge detection. Other useful operations on the image were tested, including image cropping, image resizing, image, histogram equalization and altering image brightness
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Signal, Audio and Image Processing - Dr. Hidaia Mahmood Alassouli
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Table of Contents
1. Introduction
2. Theory Related To Project
2.1 What is the signal Processing Toolbox in Matlab
2.2 Signal and Linear System Models
2.3 Filter Implementation and Analysis
2.4 Digital Filter Design Methods
2.5 Transforms
2.6 Frequency Response
2.7 GUIs for Filter Design, Analysis, and Visualization……………18
3. Descrption of Project and Results and Analysis
3.1 Part I: Design different filters for filtering sinusoidal signal
3.2 Part II: Audio Processing
3.2.2 Audio Compressing
3.3 Part III: Image Processing
3.3.1 Filtering
3.3.2 Other operations: Image cropping, sizing and histogram equalization, changing brightness
4. Conclusion
5. References
1. Introduction:
This project shows some selected signal techniques, including image and audio processing, using the Matlab digital signal processing and image processing toolboxes. The project is divided to 3 parts
Part I includes design and implementation of different types of filters for filtering signal that has different sinusoidal frequency components or noise. The comparison was made between FIR low pass flter, butterworth filter, Chebycheve Type I low pass filter and Chebycheve Type II low pass filter. Then different types of IIR Butterworth filters were designed and implemented to filter a signal that has many harmonics components, including low pass filter, high pass filter, stop band filter and band pass filter.
Part II examined audio filtering in the sense of specific frequency suppression and extraction. There are many different types of filters available for the construction of filters. We will specifically use the Butterworth filter. An audio signal was read and different types of filters, including low pass filter, high pass filter, stop band filter and band pass filter, were designed and implemented in order to filter the audio signal from some frequency bands:
Then the discrete cosine transform compression examined on the audio signal at different compression rates: 50%, 75% , 87.5%
Part III deals with image processing; the project shows examples in smoothing, sharpening, and edge detection. Other useful operations on the image were tested, including image cropping, image resizing, image, histogram equalization and altering image brightness.
2.Theory Related To Project:
2.1 What is the signal Processing Toolbox in Matlab:
The Signal Processing Toolbox is a collection of tools built on the MATLAB® numeric computing environment. The toolbox supports a wide range of signal processing operations, from waveform generation to filter design and implementation, parametric modeling, and spectral analysis. The toolbox provides two categories of tools: Command line functions in the following categories:
Analog and digital filter analysis
Digital filter implementation
FIR and IIR digital filter design
Analog filter design Filter discretization
Spectral Windows Transforms Cepstral analysis
Statistical signal processing and spectral analysis
Parametric modeling
Linear Prediction
Waveform generation
A suite of interactive graphical user interfaces for
Filter design and analysis Window design and analysis
Signal plotting and analysis
Spectral analysis Filtering signals
2.2 Signal and Linear System Models:
The Signal Processing Toolbox provides a broad range of models for representing signals and linear time-invariant systems, including representations for transfer functions, state space, and zero-pole gain. The toolbox also includes