Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Categorization of sound events: environmental, mechanical, and human-generated
• Review of key use cases: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data 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 Annotation

• Utilizing UrbanSound8K, ESC-50, and custom datasets
• Labeling sound events and defining temporal boundaries
• Techniques for balancing datasets and augmenting audio

Building Audio Classification Models

• Application of convolutional neural networks (CNNs) to audio data
• Model inputs: comparing raw waveforms against extracted features
• Managing loss functions, evaluation metrics, and overfitting

Event Detection and Temporal Localization

• Implementing frame-based and segment-based detection strategies
• Refining detections through thresholds and smoothing post-processing
• Visualizing predictions on audio timelines

Advanced Topics and Real-Time Processing

• Applying transfer learning in low-data scenarios
• Deploying models using TensorFlow Lite or ONNX
• Handling streaming audio processing and latency constraints

Project Development and Application Scenarios

• Designing an end-to-end pipeline from ingestion to classification
• Creating a proof-of-concept for surveillance, quality control, or monitoring
• Integrating logging, alerting, and dashboard or API connectivity

Summary and Next Steps