Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

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

Audio Data and Feature Extraction

• Various 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 datasets such as UrbanSound8K, ESC-50, and custom collections
• Annotating sound events and their temporal boundaries
• Dataset balancing and audio augmentation techniques

Building Audio Classification Models

• Application of convolutional neural networks (CNNs) for audio tasks
• Model inputs: raw waveforms versus extracted features
• Selection of loss functions, evaluation metrics, and managing overfitting

Event Detection and Temporal Localization

• Detection strategies based on frames and segments
• Post-processing techniques using thresholds and smoothing
• Visualization of predictions along audio timelines

Advanced Topics and Real-Time Processing

• Applying transfer learning in scenarios with limited data
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio processing and latency constraints

Project Development and Application Scenarios

• Designing an end-to-end pipeline from data ingestion to classification
• Creating proof-of-concept solutions for surveillance, quality control, or monitoring
• Integration of logging, alerting, and connections to dashboards or APIs

Summary and Next Steps