Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Categorization of sound events: environmental, mechanical, and human-generated
• Overview of key use cases: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data and Feature Extraction

• Analysis of audio file types and formats
• Considerations for sampling rate, windowing, and frame size
• Extraction of MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation

• Utilizing UrbanSound8K, ESC-50, and custom datasets
• Labeling sound events and defining temporal boundaries
• Strategies for dataset balancing and audio augmentation

Building Audio Classification Models

• Leveraging convolutional neural networks (CNNs) for audio tasks
• Evaluating model inputs: raw waveforms versus extracted features
• Understanding loss functions, evaluation metrics, and overfitting prevention

Event Detection and Temporal Localization

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

Advanced Topics and Real-Time Processing

• Applying transfer learning in low-data scenarios
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio processing and latency optimization

Project Development and Application Scenarios

• Architecting end-to-end pipelines from ingestion to classification
• Crafting proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting, and dashboard or API connectivity

Summary and Next Steps