Audio Classification and Event Detection with ML Training Course

Basics of Audio Classification

• Categorizing sound events: environmental, mechanical, and human-originated
• Key application areas: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data Handling and Feature Extraction

• Variations in audio file types and formats
• Considerations for sampling rates, windowing, and frame sizes
• Extracting MFCCs, chroma features, and mel-spectrograms

Data Curation and Annotation

• Utilizing datasets like UrbanSound8K, ESC-50, and custom collections
• Annotating sound events and defining temporal boundaries
• Strategies for dataset balancing and audio augmentation

Constructing Audio Classification Models

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

Event Detection and Temporal Localization

• Strategies for frame-based and segment-based detection
• Refining detection results through thresholding and smoothing
• Visualizing predictions along audio timelines

Advanced Techniques and Real-Time Processing

• Applying transfer learning in low-data environments
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio and managing latency

Project Implementation and Use Cases

• Architecting end-to-end pipelines from ingestion to classification
• Building proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting, and connections to dashboards or APIs

Conclusion and Future Directions