دانلود کتاب Digital Signal Processing in Audio and Acoustical Engineering

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Cover
Half Title
Title Page
Copyright Page
Contents
Preface
About the Authors
Chapter 1: Acoustic Signals and Audio Systems
1.1. Signals and Systems
1.2. Types of Systems by Properties
1.3. Types of Signals
1.3.1. Deterministic Signals
1.3.2. Some Special Testing Signals
1.3.3. Random Signals
1.4. Statistics of Random Signals
1.4.1. Probability Density Function and Moments
1.4.2. Lag Statistical Analysis and Correlation Functions
1.4.3. Gaussian Distribution and Central Limit Theorem
1.5. Signals in Transformed Frequency Domains
1.5.1. Fourier and Laplace Transforms
1.5.2. Signal Statistics in the Frequency Domain
1.5.3. Input-Output Relationships of LTI Systems
Summary
Bibliography and Extended Reading
Exploration
Chapter 2: Sampling Quantization and Discrete Fourier
2.1. Sampling
2.1.1. Time Discretization
2.1.2. Aliasing
2.2. Fourier
2.3. Fourier Series of Periodic, Discrete-Time Signals
2.4. Practical FFTs
2.4.1. Positive and Negative Frequencies
2.4.2. Windowing
2.4.3. The Convolution Theorem
2.4.4. Avoiding Spectral Smearing—More Windows
2.5. Estimating Statistics Using Fourier Methods
2.5.1. Cross Power Spectral Density Function
2.5.2. Estimating the CPSD
2.6. Transfer Function Measurement in Noise
2.6.1. The Ordinary Coherence Function
Summary
Bibliography and Extended Reading
Exploration
Chapter 3: DSP in Acoustical Transfer Function Measurements
3.1. Acoustical Transfer Function Measurement Problems
3.2. Transfer Function Measurement Using MLS
3.2.1. Maximum Length Sequences (MLSs)
3.2.2. Some Useful Properties of MLS
3.2.3. Measure Once
3.2.4. No Truncation Errors
3.2.5. Crest Factor
3.3. Transfer Function Measurement Using Swept Sine Waves
3.3.1. Matched Filtering
Summary
Bibliography and Extended Reading
Exploration and Mini Project
Chapter 4: Digital Filters and z-Transform
4.1. General Introduction to Digital Filters
4.2. Finite Impulse Response (FIR) Filters
4.3. z-Transform and Transfer Function
4.4. Zero-Pole Plots
4.5. Infinite Impulse Response (IIR) Filters
4.6. Stability
4.7. Bilinear IIR Filters (BILINS)
4.8. Biquadratic IIR Filter Design (Biquads)
4.9. IIR Filter Design Using the Bilinear Transform
4.9.1. Butterworth Low Pass Filters
4.10. FIR Filter Design—The Fourier Transform Method
4.10.1. Time/Frequency Effects
4.10.2. Least Square Estimates of Transfer Functions
4.10.3. Practical Filters Have Real Coefficients
4.10.4. Zero Phase and Linear Phase Filters
4.10.5. Recapitulation: FIR Filter Design Procedure
Summary
Bibliography and Extended Reading
Exploration
Chapter 5: Audio Codecs
5.1. Audio Codecs
5.2. Quantization and PCM family encoding
5.2.1. Quantization as a Noise Source
5.2.2. Quantization as a Distortion Process
5.2.3. Dynamic Range due to Quantization
5.3. Dither
5.4. From PCM to DPCM
5.5. Oversampling and Low Bit Converters
5.6. One-Bit Conversion, Sigma-Delta Modulation
5.7. Lossy Codecs and MPEG Codecs
Summary
References
Bibliography and Extended Reading
Exploration and Mini Project
Chapter 6: DSP in Binaural Hearing and Microphone Arrays
6.1. Head Related Transfer Function and Binaural Signal Processing
6.1.1. Head Related Transfer Functions (HRTFs)
6.1.2. HRTF Data
6.1.3. Application Scenarios
6.2. Microphone Arrays and Delay-Sum Beamformers
Summary
References
Bibliography and Extended Reading
Exploration
Chapter 7: Adaptive Filters
7.1. General Model of LMS Adaptive Filters
7.2. Four Generic Types of Adaptive Filters
7.2.1. System Identification
7.2.2. Inverse Modelling
7.2.3. Noise or Interference Cancellation
7.2.4. Linear Prediction
7.3. From Optimal Filter to Least Mean Square (LMS) Adaptive Algorithms
7.3.1. Concept of Optimal Filters
7.3.2. A Discrete-Time Formulation of Optimal Filter
7.3.3. Adaptive Methods and LMS Algorithm
7.4. Genetic Algorithms: Another Adaptive Technique Genetic Algorithms
7.4.1. Genetic Algorithms
Summary
Reference
Bibliography and Extended Reading
Exploration
Chapter 8: Machine Learning in Acoustic DSP
8.1. General Concept of Acoustic Pattern Recognition
8.2. Common Acoustic Features
8.2.1. Acoustic Features and Feature Spaces
8.2.1.1. Time-Domain Features
8.2.1.2. Frequency-Domain Features
8.2.2. Time-Frequency Domain
8.2.2.1. Mel-Frequency Cepstrum Coefficients
8.3. Decision Making by Machine Learning
8.3.1. Machine Learning
8.3.2. Artificial Neural Network
8.3.2.1. Neuron Models
8.3.3. Topology of Artificial Neural Network
8.3.4. Supervised Learning Rule
8.4. Training, Testing and Validation
8.4.1. Training and Testing
8.4.1.1. Holdout Cross-Validation
8.4.1.2. K-Fold Cross-Validation
8.4.2. Over-Fitting and Under-Fitting
8.4.3. Stop Criterion, Step Size, and Restart
8.5. Speech Recognition
8.6. Speaker Recognition
8.7. Music Information Retrieval
8.8. Machine Audition of Acoustics
8.8.1. Acoustic Transmission Channels and Acoustic Parameters
8.8.2. Extraction of Reverberation Time from Discrete Utterances
8.8.3. Estimation of Speech Transmission Index from Running Speech
8.8.4. Estimation of Reverberation Time from Running Speech
8.8.5. Using Music as Stimuli
8.9. Blind Estimation with a Parametric Model: Maximum Likelihood Estimation
Summary
References
Bibliography and Extended Reading
Recommended Software and Tool Kits
Exploration and Mini Projects
Chapter 9: Unsupervised Learning and Blind Source Separation
9.1. Hebbian Learning (Self-Organised Learning)
9.2. PCA Neural Networks
9.2.1. Hebbian Maximum Eigenfilter and PCA
9.2.2. Generalised Hebbian Algorithm and PCA Network
9.3. ICA Neural Networks and Blind Source Separation
9.4. Blind Estimation of Room Acoustic Parameters Using a PCA Network as a Feature Extractor
Summary
References
Bibliography and Extended Reading
Recommended Software and Tool Kits
Exploration and Mini Project
Chapter 10: DSP in Hearing Aids
10.1. Technical Challenges of Hearing Aids
10.2. Audiometry and Hearing Aid Fitting
10.3. Filter Bank and Multi-Band Compression
10.3.1. Filter Bank
10.3.2. Compression Channel
10.4. Acoustic Feedback Cancellation
10.5. Transposition and Frequency Lowering
10.6. Other Add-N Features
Summary
References
Index