Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Sound event types: environmental, mechanical, human-generated
• Overview of use cases: surveillance, monitoring, automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data and Feature Extraction

• Varieties of audio files and formats
• Considerations for sampling rate, windowing, and frame size
• Extracting MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation

• Utilizing UrbanSound8K, ESC-50, and custom datasets
• Annotating sound events and temporal boundaries
• Dataset balancing and audio augmentation techniques

Building Audio Classification Models

• Employing convolutional neural networks (CNNs) for audio analysis
• Model inputs: raw waveforms versus extracted features
• Loss functions, evaluation metrics, and managing overfitting

Event Detection and Temporal Localization

• Frame-based and segment-based detection strategies
• Post-processing detections using thresholds and smoothing
• Visualizing predictions on audio timelines

Advanced Topics and Real-Time Processing

• Applying transfer learning in low-data scenarios
• Deploying models with TensorFlow Lite or ONNX
• Streaming audio processing and latency considerations

Project Development and Application Scenarios

• Designing a complete pipeline: from ingestion to classification
• Developing proof-of-concept solutions for surveillance, quality control, or monitoring
• Implementing logging, alerting, and integration with dashboards or APIs

Summary and Next Steps