Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Categorizing sound events: environmental, mechanical, and human-generated
• Key use cases: surveillance, continuous monitoring, and automation
• Distinguishing between classification, detection, and segmentation

Audio Data Handling and Feature Extraction

• Common audio file types and formats
• Considerations for sampling rates, windowing, and frame sizes
• Techniques for extracting MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation Processes

• Working with datasets like UrbanSound8K, ESC-50, and custom collections
• Annotating sound events and defining temporal boundaries
• Strategies for dataset balancing and audio augmentation

Constructing Audio Classification Models

• Leveraging convolutional neural networks (CNNs) for audio tasks
• Choosing between raw waveforms and extracted features as model inputs
• Selecting appropriate loss functions, evaluation metrics, and mitigating overfitting

Event Detection and Temporal Localization

• Implementing frame-based and segment-based detection strategies
• Refining detections through thresholding and smoothing techniques
• Visualizing predictions across audio timelines

Advanced Concepts and Real-Time Processing

• Applying transfer learning in data-scarce scenarios
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio and managing latency constraints

Project Development and Practical Applications

• Architecting complete pipelines from data ingestion to classification
• Building proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting systems, and dashboards or APIs

Wrap-Up and Future Directions