Audio Classification and Event Detection with ML Training Course

Basics of Audio Classification

• Categories of sound events: environmental, mechanical, and human-made
• Summary of applications: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data and Feature Extraction

• Various audio file types and formats
• Considerations regarding sampling rates, windowing, and frame sizes
• Extraction of MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation

• Utilizing UrbanSound8K, ESC-50, and proprietary datasets
• Labeling sound events and defining temporal boundaries
• Dataset balancing and audio augmentation techniques

Constructing Audio Classification Models

• Application of convolutional neural networks (CNNs) to audio data
• Input strategies: raw waveforms versus extracted features
• Loss functions, evaluation metrics, and managing overfitting

Event Detection and Temporal Localization

• Frame-based and segment-based detection approaches
• Refining detections through thresholding and smoothing
• Mapping predictions onto audio timelines for visualization

Advanced Concepts and Real-Time Processing

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

Project Development and Real-World Scenarios

• Architecting a complete pipeline from data ingestion to classification
• Creating proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting, and dashboard or API connections

Conclusion and Future Directions