Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Types of sound events: environmental, mechanical, and human-generated
• Use case overview: surveillance, monitoring, and automation
• Distinguishing audio classification from detection and segmentation

Audio Data and Feature Extraction

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

Data Preparation and Annotation

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

Building Audio Classification Models

• Applying convolutional neural networks (CNNs) to audio data
• Model inputs: comparing raw waveforms with 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 detections using thresholds and smoothing techniques
• Visualizing predictions on audio timelines

Advanced Topics and Real-Time Processing

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

Project Development and Application Scenarios

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

Summary and Next Steps