Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

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

Audio Data and Feature Extraction

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

Data Preparation and Annotation

• Utilizing datasets such as UrbanSound8K, ESC-50, and custom collections
• Annotation of sound events and their temporal boundaries
• Dataset balancing and audio augmentation strategies

Building 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 management of overfitting

Event Detection and Temporal Localization

• Strategies for frame-based and segment-based detection
• Post-processing of detections through thresholds and smoothing techniques
• Visualization of predictions across audio timelines

Advanced Topics and Real-Time Processing

• Employing 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

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

Summary and Next Steps