Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

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

Audio Data and Feature Extraction

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

Data Preparation and Annotation

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

Building Audio Classification Models

• Application of convolutional neural networks (CNNs) for audio analysis
• Model input variations: raw waveforms versus extracted features
• Selection of loss functions, evaluation metrics, and management of overfitting

Event Detection and Temporal Localization

• Implementation of frame-based and segment-based detection strategies
• Post-processing of detections using thresholds and smoothing algorithms
• Visualization of predictions along audio timelines

Advanced Topics and Real-Time Processing

• Transfer learning approaches for low-data scenarios
• Model deployment using TensorFlow Lite or ONNX
• Streaming audio processing and latency optimization

Project Development and Application Scenarios

• Designing a comprehensive pipeline from data ingestion to classification
• Developing proofs-of-concept for surveillance, quality control, or monitoring systems
• Integration of logging, alerting, and connectivity with dashboards or APIs

Summary and Next Steps