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 including surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data and Feature Extraction

• Understanding 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 Annotation

• Utilizing datasets such as UrbanSound8K, ESC-50, and custom collections
• Techniques for labeling sound events and defining temporal boundaries
• Strategies for dataset balancing and audio augmentation

Building Audio Classification Models

• Application of convolutional neural networks (CNNs) to audio data
• Evaluating model inputs: raw waveforms versus extracted features
• Management of loss functions, evaluation metrics, and overfitting

Event Detection and Temporal Localization

• Implementing frame-based and segment-based detection strategies
• Refining detections through thresholding and smoothing techniques
• Visualizing prediction results on audio timelines

Advanced Topics and Real-Time Processing

• Applying transfer learning to address low-data scenarios
• Model deployment using TensorFlow Lite or ONNX
• Managing streaming audio processing and latency considerations

Project Development and Application Scenarios

• Architecting a comprehensive pipeline from ingestion to classification
• Creating proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting, and connections to dashboards or APIs

Summary and Next Steps