Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

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

Audio Data Management and Feature Extraction

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

Data Preparation and Annotation Processes

• Utilizing datasets such as UrbanSound8K, ESC-50, and custom collections
• Labeling sound events and defining temporal boundaries
• Techniques for dataset balancing and audio augmentation

Constructing Audio Classification Models

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

Event Detection and Temporal Localization

• Strategies for frame-based and segment-based detection
• Post-processing techniques involving thresholds and smoothing
• Visualization of predictions along audio timelines

Advanced Concepts and Real-Time Processing

• Transfer learning strategies for scenarios with limited data
• Model deployment using TensorFlow Lite or ONNX
• Streaming audio processing and managing latency

Project Development and Application Scenarios

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

Summary and Future Directions