Audio Classification and Event Detection with ML Training Course

Audio Classification Fundamentals

• Categorization of sound events: environmental, mechanical, and human-generated.
• Exploration of key use cases including 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.
• Techniques for extracting MFCCs, chroma features, and mel-spectrograms.

Data Preparation and Annotation Processes

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

Developing Audio Classification Models

• Application of convolutional neural networks (CNNs) to audio data.
• Input structures: comparing raw waveforms versus extracted features.
• Selection of loss functions, evaluation metrics, and managing overfitting.

Event Detection and Temporal Localization

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

Advanced Concepts and Real-Time Processing

• Leveraging transfer learning for scenarios with limited data.
• Model deployment using TensorFlow Lite or ONNX.
• Streaming audio processing and latency optimization.

Project Development and Scenario Implementation

• Architecting a complete pipeline 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