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.
• Techniques for extracting MFCCs, chroma features, and mel-spectrograms.

Data Preparation and Annotation

• Utilizing datasets such as UrbanSound8K, ESC-50, and custom collections.
• Labeling sound events and defining temporal boundaries.
• Strategies for balancing datasets and applying audio augmentation.

Building Audio Classification Models

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

Event Detection and Temporal Localization

• Implementing frame-based and segment-based detection strategies.
• Post-processing detections using thresholds and smoothing techniques.
• Visualizing predictions along 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

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

Summary and Next Steps