Signal Processing with AI using Python
Duration – 5 Days.
Objectives
- Understand the fundamentals of digital signal processing (DSP) and its Python implementation
- Apply signal processing techniques for analysis, denoising, and feature extraction
- Build machine learning (ML) and deep learning (DL) models for time-series and audio signals
- Visualize, evaluate, and deploy AI models for real-world signal applications
- Gain hands-on experience using Python libraries for signal analysis and AI
Tools & Platforms
- Python 3.x with Jupyter Notebook or VS Code
- NumPy, SciPy – numerical computing & signal processing
- matplotlib, seaborn, plotly – data visualization
- librosa, soundfile – audio processing
- scikit-learn – classical ML models
- PyTorch / TensorFlow / Keras – deep learning models
- pywavelets – wavelet transforms
- Optional: pandas – data handling for multi-channel signals
Pre-requisites
- Basic Python programming knowledge (variables, loops, functions, classes)
- Familiarity with Jupyter Notebook or any Python IDE
- Understanding of basic linear algebra and statistics
- Fundamental knowledge of signals (optional but helpful)
Take away
- Be proficient in importing, visualizing, and processing real-world signals in Python
- Apply filtering, FFT/STFT, wavelet transforms, and feature extraction techniques
- Implement classical ML algorithms and deep learning models for signal classification, prediction, and anomaly detection
- Perform hyperparameter tuning, model evaluation, and simulate deployment for AI-enabled signal processing
- Complete at least one mini-project demonstrating end-to-end signal processing and AI workflow
Day 2 – Advanced Signal Processing Techniques
Theory
- Signal Denoising & Smoothing Techniques
- Moving average, Savitzky-Golay, wavelet denoising
- Time-Frequency Analysis
- Short-time Fourier Transform (STFT)
- Continuous and discrete wavelet transforms
- Signal Compression & Dimensionality Reduction
- PCA, t-SNE for signal features
- Real-Time Signal Processing Basics
- Streaming data, buffer management, windowing
Hands-On Labs
- Noise removal & signal enhancement
- Extracting features from time-frequency domain
- PCA and visualization of multi-channel signals
- Implementing basic real-time signal streaming simulation
Day 3 – AI & Machine Learning for Signals
Theory
- Introduction to AI in Signal Processing
- ML vs DL for time-series signals
- Classification, regression, and anomaly detection
- Signal Preprocessing for AI
- Normalization, augmentation, segmentation, denoising
- Classical Machine Learning Models for Signals
- SVM, kNN, Random Forests
- Deep Learning Models for 1D Signals
- 1D CNNs for time-series classification
- RNNs/LSTMs for sequential data
Hands-On Labs
- Preparing signal datasets for ML/DL
- Training an ML classifier on extracted features (audio digits, ECG signals)
- Building a simple 1D CNN in PyTorch/TensorFlow for signal classification
- Visualizing training progress and model evaluation metrics
Day 4 – Advanced Deep Learning for Signal Processing
Theory
- CNN Architectures for 1D and 2D Signals
- Temporal CNNs, spectrogram-based CNNs
- RNN/LSTM & GRU for sequential signals
- Attention mechanisms in signal processing
- Transfer Learning & Pretrained Models for Signals
Hands-On Labs (3.5 hrs):
- Implementing LSTM/GRU models for ECG or audio sequence prediction
- Using spectrograms as input to CNNs
- Hyperparameter tuning and model regularization
- Model saving, loading, and inference for new signals
Day 5 – Applications, Projects & Deployment
Theory
- Real-World Applications of AI in Signals
- Speech recognition & audio event detection
- Biomedical signal analysis (ECG, EEG)
- IoT sensor signal intelligence
- Radar & communication signals with AI
- Deployment Considerations
- GPU acceleration, ONNX, TensorFlow Lite, Edge deployment
- Performance optimization & best practices
Hands-On Labs / Mini Projects
- Project 1: Audio/Speech Classification using CNN in Python
- Project 2: ECG Anomaly Detection using LSTM
- Model evaluation, visualization, and deployment simulation
Additional Notes:
- Daily Q&A Sessions
- Regular Assignments
- Final Assessment
Note: Hands-on training will be conducted during the sessions using the tools listed above, subject to availability.
