Digital Signal Processing
From Fundamentals to Advanced Applications
Master digital signal processing from fundamentals to advanced applications. Interactive visualizations, Python implementations with NumPy/SciPy, and real-world projects in audio, image, communications, and biomedical signal processing.
Foundations— Signals, math basics, and sampling.
Introduction to Digital Signal Processing
What is DSP and why it matters in the modern world
Mathematical Foundations
Essential mathematics for signal processing
Continuous-Time Signals and Systems
Foundation concepts before going digital
Sampling and Quantization
Converting analog signals to digital
Time Domain— Discrete signals, systems, convolution.
Discrete-Time Signals
Working with digital sequences
Discrete-Time Systems
Processing digital signals
Convolution
The fundamental operation in signal processing
Correlation and Applications
Measuring signal similarity
Frequency Domain— Fourier transforms, FFT, windowing.
Fourier Series
Decomposing periodic signals into harmonics
Continuous Fourier Transform
Frequency analysis of aperiodic signals
Discrete-Time Fourier Transform (DTFT)
Frequency analysis of discrete signals
Discrete Fourier Transform (DFT)
Practical frequency analysis for computers
Fast Fourier Transform (FFT)
Efficient computation of the DFT
Windowing and Spectral Analysis
Practical spectrum analysis techniques
Z-Transform— System analysis and transfer functions.
The Z-Transform
The fundamental tool for discrete system analysis
System Analysis Using Z-Transform
Analyzing systems in the z-domain
Analysis of LTI Systems
Complete system characterization
Relationship Between Transforms
Connecting s-domain and z-domain
Filter Design— FIR, IIR, structures, implementation.
Introduction to Digital Filters
Fundamentals of filter design
FIR Filter Design
Designing finite impulse response filters
IIR Filter Design
Designing infinite impulse response filters
Filter Structures and Implementation
Realizing filters in hardware and software
Finite Wordlength Effects
Practical considerations for fixed-point implementation
Special Filters
Specialized filter types and applications
Spectral Methods— PSD, parametric, time-frequency, wavelets.
Power Spectrum Estimation
Estimating signal power distribution
Parametric Spectral Estimation
Model-based spectral analysis
Time-Frequency Analysis
Analyzing non-stationary signals
Wavelet Transform
Multi-resolution signal analysis
Multirate & Adaptive— Sample rate conversion, adaptive filters.
Multirate Signal Processing Fundamentals
Changing sample rates efficiently
Polyphase Filters
Efficient multirate filter implementations
Filter Banks
Analysis and synthesis filter systems
Adaptive Filters
Self-adjusting filter systems
Adaptive Filter Applications
Real-world adaptive filtering systems
Statistical DSP— Random signals, optimal and Kalman filtering.
Random Signals
Statistical characterization of signals
Optimal Filtering
Minimum mean square error estimation
Kalman Filtering
Optimal recursive state estimation
Detection and Estimation
Statistical inference for signals
Applications— Audio, speech, image, communications, biomedical, ML.
Audio Signal Processing
Processing music and sound
Speech Processing
Analysis and synthesis of speech
Image Processing Fundamentals
2D signal processing for images
Advanced Image Processing
Advanced techniques for image analysis
Communications Systems
DSP for digital communications
Radar and Sonar Signal Processing
Remote sensing applications
Biomedical Signal Processing
Processing physiological signals
Machine Learning for DSP
AI-powered signal processing
Implementation— Hardware, fixed-point, real-time systems.
DSP Hardware and Architectures
Hardware for signal processing
Fixed-Point DSP Implementation
Efficient numerical representations
Real-Time DSP Systems
Building real-time signal processing systems
Where the book lands in practice.
334 sections. Begin with one.
Chapter 1 — Introduction to Digital Signal Processing — is where every reader starts.