Audio Classification and Event Detection with ML Training Course

Core Concepts of Audio Classification

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

Audio Data Handling and Feature Engineering

• Common audio file types and formats
• Considerations for sampling rate, windowing, and frame size
• Techniques for extracting MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation Strategies

• Working with UrbanSound8K, ESC-50, and custom datasets
• Annotating sound events and defining temporal boundaries
• Strategies for balancing datasets and applying audio augmentation

Developing Audio Classification Models

• Applying convolutional neural networks (CNNs) to audio tasks
• Choosing model inputs: raw waveforms versus pre-extracted features
• Understanding loss functions, evaluation metrics, and managing overfitting

Event Detection and Temporal Localization

• Implementing frame-based and segment-based detection approaches
• Refining detections through thresholding and smoothing techniques
• Visualizing model predictions across audio timelines

Advanced Techniques and Real-Time Integration

• Using transfer learning to overcome limited data scenarios
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio processing and minimizing latency

Project Construction and Practical Applications

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

Conclusion and Future Directions