Note: Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Preface -- Editors -- Contributors -- 1. Introduction to Signal Processing and Machine Learning -- 1.1 Introduction -- 1.2 Basic Terminologies -- 1.2.1 Signal Processing -- 1.2.1.1 Continuous and Discrete Signals -- 1.2.1.2 Sampling and Quantization -- 1.2.1.3 Change of Basis -- 1.2.1.4 Importance of Time Domain and Frequency Domain Analyses -- 1.2.2 Machine Learning -- 1.3 Distance-Based Signal Classification, Nearest Neighbor Classifier, and Hilbert Space -- 1.3.1 Distance-Based Signal Classification -- 1.3.1.1 Metric Space -- 1.3.1.2 Normed Linear Space -- 1.3.1.3 Inner Product Space -- 1.3.2 Nearest Neighbor Classification -- 1.3.3 Hilbert Space -- 1.4 Fusion of Machine Learning in Signal Processing -- 1.5 Benefits of Adopting Machine Learning in Signal Processing -- 1.6 Conclusion -- References -- 2. Learning Theory (Supervised/Unsupervised) for Signal Processing -- 2.1 Introduction -- 2.1.1 Signal Processing -- 2.2 Machine Learning -- 2.2.1 Why Do We Need ML for Signal Processing? -- 2.2.2 Speaker ID - A Utilization of ML Calculations in Sign Handling -- 2.2.3 Discourse and Audio Processing -- 2.2.4 Discourse Recognition -- 2.2.5 Listening Devices -- 2.2.6 Independent Driving -- 2.2.7 Picture Processing and Analysis -- 2.2.8 Wearables -- 2.2.9 Information Science -- 2.2.10 Wireless Systems and Networks -- 2.3 Machine Learning Algorithms -- 2.4 Supervised Learning -- 2.5 Unsupervised Learning -- 2.6 Semi-Supervised Learning -- 2.7 Reinforcement Learning -- 2.8 Use Case of Signal Processing Using Supervised and Unsupervised Learning -- 2.8.1 Features and Classifiers -- 2.8.2 Linear Classifiers -- 2.8.3 Decision Hyperplanes -- 2.8.4 Least Squares Methods -- 2.8.5 Mean Square Estimation -- 2.8.6 Support Vector machines -- 2.8.7 Non-Linear Regression. , 2.8.8 Non-Linearity of Activation Functions -- 2.8.8.1 Sigmoid Function -- 2.8.8.2 Rectified Linear Unit (ReLU) -- 2.8.9 Classification -- 2.8.9.1 Linear Classification -- 2.8.9.2 Two-Class Classification -- 2.8.9.3 Geometrical Interpretation of Derivatives -- 2.8.9.4 Multiclass Classification: Loss Function -- 2.8.10 Mean Squared Error -- 2.8.11 Multilabel Classification -- 2.8.12 Gradient Descent -- 2.8.12.1 Learning Rate -- 2.8.13 Hyperparameter Tuning -- 2.8.13.1 Validation -- 2.8.14 Regularization -- 2.8.14.1 How Does Regularization Work? -- 2.8.15 Regularization Techniques -- 2.8.15.1 Ridge Regression (L2 Regularization) -- 2.8.16 Lasso Regression (L1 Regularization) -- 2.8.17 K-Means Clustering -- 2.8.18 The KNN Algorithm -- 2.8.19 Clustering -- 2.8.20 Clustering Methods -- 2.9 Deep Learning for Signal Data -- 2.9.1 Traditional Time Series Analysis -- 2.9.2 Recurrence Domain Analysis -- 2.9.3 Long Short-Term Memory Models for Human Activity Recognition -- 2.9.4 External Device HAR -- 2.9.5 Signal Processing on GPUs -- 2.9.6 Signal Processing on FPGAs -- 2.9.7 Signal Processing is coming to the Forefront of Data Analysis -- 2.10 Conclusion -- References -- 3. Supervised and Unsupervised Learning Theory for Signal Processing -- 3.1 Introduction -- 3.1.1 Supervised Learning -- 3.1.2 Unsupervised Learning -- 3.1.3 Reinforcement Learning -- 3.1.4 Semi-Supervised Learning -- 3.2 Supervised Learning Method -- 3.2.1 Classicfiation Problems -- 3.2.2 Regression Problems -- 3.2.3 Examples of Supervised Learning -- 3.3 Unsupervised Learning Method -- 3.3.1 Illustrations of Unsupervised Learning -- 3.4 Semi-Supervised Learning Method -- 3.5 Binary Classification -- 3.5.1 Different Classes -- 3.5.2 Classification in Preparation -- 3.5.2.1 Logistic Regression Model -- 3.5.2.2 Odds Ratio -- 3.5.2.3 Logit Function -- 3.5.2.4 The Sigmoid Function. , 3.5.2.5 Support Vector Machines -- 3.5.2.6 Maximum Margin Lines -- 3.6 Conclusion -- References -- 4. Applications of Signal Processing -- 4.1 Introduction -- 4.2 Audio Signal Processing -- 4.2.1 Machine Learning in Audio Signal Processing -- 4.2.1.1 Spectrum and Cepstrum -- 4.2.1.2 Mel Frequency Cepstral Coefficients -- 4.2.1.3 Gammatone Frequency Cepstral Coefficients -- 4.2.1.4 Building the Classifier -- 4.3 Audio Compression -- 4.3.1 Modeling and Coding -- 4.3.2 Lossless Compression -- 4.3.3 Lossy Compression -- 4.3.4 Compressed Audio with Machine Learning Applications -- 4.4 Digital Image Processing -- 4.4.1 Fields Overlapping with Image Processing -- 4.4.2 Digital Image Processing System -- 4.4.3 Machine Learning with Digital Image Processing -- 4.4.3.1 Image Classification -- 4.4.3.2 Data Labelling -- 4.4.3.3 Location Detection -- 4.5 Video Compression -- 4.5.1 Video Compression Model -- 4.5.2 Machine Learning in Video Compression -- 4.5.2.1 Development Savings -- 4.5.2.2 Improving Encoder Density -- 4.6 Digital Communications -- 4.6.1 Machine Learning in Digital Communications -- 4.6.1.1 Communication Networks -- 4.6.1.2 Wireless Communication -- 4.6.1.3 Smart Infrastructure and IoT -- 4.6.1.4 Security and Privacy -- 4.6.1.5 Multimedia Communication -- 4.6.2 Healthcare -- 4.6.2.1 Personalized Medical Treatment -- 4.6.2.2 Clinical Research and Trial -- 4.6.2.3 Diagnosis of Disease -- 4.6.2.4 Smart Health Records -- 4.6.2.5 Medical Imaging -- 4.6.2.6 Drug Discovery -- 4.6.2.7 Outbreak Prediction -- 4.6.3 Seismology -- 4.6.3.1 Interpreting Seismic Observations -- 4.6.3.2 Machine Learning in Seismology -- 4.6.4 Speech Recognition -- 4.6.5 Computer Vision -- 4.6.6 Economic Forecasting -- 4.7 Conclusion -- References -- 5. Dive in Deep Learning: Computer Vision, Natural Language Processing, and Signal Processing -- 5.1 Deep Learning: Introduction. , 5.2 Past, Present, and Future of Deep- Learning -- 5.3 Natural Language Processing -- 5.3.1 Word Embeddings -- 5.3.1.1 Word2vec -- 5.3.2 Global Vectors for Word Representation -- 5.3.3 Convolutional Neural Networks -- 5.3.4 Feature Selection and Preprocessing -- 5.3.4.1 Tokenization -- 5.3.4.2 Stop Word Removal -- 5.3.4.3 Stemming -- 5.3.4.4 Lemmatization -- 5.3.5 Named Entity Recognition -- 5.4 Image Processing -- 5.4.1 Introduction to Image Processing and Computer Vision -- 5.4.1.1 Scene Understanding -- 5.4.2 Localization -- 5.4.3 Smart Cities and Surveillance -- 5.4.4 Medical Imaging -- 5.4.5 Object Representation -- 5.4.6 Object Detection -- 5.5 Audio Processing and Deep Learning -- 5.5.1 Audio Data Handling Using Python -- 5.5.2 Spectrogram -- 5.5.3 Wavelet- Based Feature Extraction -- 5.5.4 Current Methods -- 5.5.4.1 Audio Classification -- 5.5.4.2 Audio Fingerprinting -- 5.5.4.3 Feature Extraction -- 5.5.4.4 Speech Classification -- 5.5.4.5 Music Processing -- 5.5.4.6 Natural Sound Processing -- 5.5.4.7 Technological Tools -- 5.6 Conclusion -- References -- 6. Brain-Computer Interfacing -- 6.1 Introduction to BCI and Its Components -- 6.1.1 BCI Components -- 6.2 Framework/Architecture of BCI -- 6.3 Functions of BCI -- 6.3.1 Correspondence and Control -- 6.3.2 Client State Checking -- 6.4 Applications of BCI -- 6.4.1 Healthcare -- 6.4.1.1 Prevention -- 6.4.1.2 Detection and Diagnosis -- 6.4.1.3 Rehabilitation and Restoration -- 6.4.2 Neuroergonomics and Smart Environment -- 6.4.3 Neuromarketing and Advertisement -- 6.4.4 Pedagogical and Self-Regulating Oneself -- 6.4.5 Games and Entertainment -- 6.4.6 Security and Authentication -- 6.5 Signal Acquisition -- 6.5.1 Invasive Techniques -- 6.5.1.1 Intracortical -- 6.5.1.2 ECoG and Cortical Surface -- 6.5.2 Noninvasive Techniques -- 6.5.2.1 Magneto-encephalography (MEG). , 6.5.2.2 fMRI (functional Magnetic Resonance Imaging) -- 6.5.2.3 fNIRS (functional Near-Infrared Spectroscopy) -- 6.5.2.4 EEG (Electroencephalogram) -- 6.6 Electrical Signal of BCI -- 6.6.1 Evoked Potential (EP) or Evoked Response -- 6.6.2 Event-Related Desynchronization and Synchronization -- 6.7 Challenges of BCI and Proposed Solutions -- 6.7.1 Challenges of Usability -- 6.7.2 Technical Issues -- 6.7.3 Proposed Solutions -- 6.7.3.1 Noise Removal -- 6.7.3.2 Disconnectedness of Multiple Classes -- 6.8 Conclusion -- References -- 7. Adaptive Filters and Neural Net -- 7.1 Introduction -- 7.1.1 Adaptive Filtering Problem -- 7.2 Linear Adaptive Filter Implementation -- 7.2.1 Stochastic Gradient Approach -- 7.2.2 Least Square Estimation -- 7.3 Nonlinear Adaptive Filters -- 7.3.1 Volterra-Based Nonlinear Adaptive Filter -- 7.4 Applications of Adaptive Filter -- 7.4.1 Biomedical Applications -- 7.4.1.1 ECG Power-Line Interference Removal -- 7.4.1.2 Maternal-Fetal ECG Separation -- 7.4.2 Speech Processing -- 7.4.2.1 Noise Cancelation -- 7.4.3 Communication Systems -- 7.4.3.1 Channel Equalization in Data Transmission Systems -- 7.4.3.2 Multiple Access Interference Mitigation in CDMA -- 7.4.4 Adaptive Feedback Cancellation in Hearing Aids -- 7.5 Neural Network -- 7.5.1 Learning Techniques in ANN -- 7.6 Single and Multilayer Neural Net -- 7.6.1 Single-Layer Neural Networks -- 7.6.2 Multilayer Neural Net -- 7.7 Applications of Neural Networks -- 7.7.1 ECG Classicafition -- 7.7.1.1 Methodology -- 7.7.2 Speech Recognition -- 7.7.2.1 Methodology -- 7.7.3 Communication Systems -- 7.7.3.1 Mobile Station Location Identification Using ANN -- 7.7.3.2 ANN-Based Call Handoff Management Scheme for Mobile Cellular Network -- 7.7.3.3 A Hybrid Path Loss Prediction Model based on Artificial  Neural Networks -- 7.7.3.4 Classification of Primary Radio Signals. , 7.7.3.5 Channel Capacity Estimation Using ANN.