Audio Classification and Event Detection with ML Training Course

Core Principles of Audio Classification

• Categorizing sound events: environmental, mechanical, and human-originated
• Exploring key use cases: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data Management and Feature Extraction

• Navigating various 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 Strategies

• Utilizing UrbanSound8K, ESC-50, and custom datasets
• Methods for labeling sound events and defining temporal boundaries
• Techniques for balancing datasets and audio augmentation

Developing Audio Classification Models

• Application of convolutional neural networks (CNNs) in audio tasks
• Evaluating model inputs: raw waveforms versus extracted features
• Managing loss functions, evaluation metrics, and overfitting risks

Event Detection and Temporal Localization

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

Advanced Concepts and Real-Time Processing

• Applying transfer learning to address limited data scenarios
• Deploying models via TensorFlow Lite or ONNX
• Managing streaming audio processing and latency requirements

Project Development and Application Scenarios

• Designing end-to-end pipelines from data ingestion to classification
• Creating proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting systems, and dashboard or API connections

Conclusion and Future Directions