Audio Classification and Event Detection with ML Training Course

Core Principles of Audio Classification

• Categorization of sound events: environmental, mechanical, and human-generated
• Exploration of application areas: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data Handling and Feature Extraction

• Overview of various audio file types and formats
• Considerations for sampling rates, windowing, and frame sizes
• Extraction of MFCCs, chroma features, and mel-spectrograms

Data Preparation and Labeling

• Utilizing UrbanSound8K, ESC-50, and bespoke datasets
• Annotating sound events and defining temporal boundaries
• Techniques for dataset balancing and audio augmentation

Developing Audio Classification Models

• Application of convolutional neural networks (CNNs) to audio data
• Comparing raw waveforms versus extracted features as model inputs
• Selection of loss functions, evaluation metrics, and mitigating overfitting

Event Detection and Temporal Localization

• Strategies for frame-based and segment-based detection
• Post-processing methods involving thresholds and smoothing techniques
• Visualization of predictions across audio timelines

Advanced Concepts and Real-Time Processing

• Applying transfer learning in low-data contexts
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio and managing latency considerations

Project Development and Application Scenarios

• Architecting a complete pipeline from data ingestion to classification
• Creating proof-of-concepts for surveillance, quality control, or monitoring systems
• Implementing logging, alerting, and integration with dashboards or APIs

Conclusion and Future Directions