Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Types 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

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

Data Preparation and Annotation

• Utilising UrbanSound8K, ESC-50, and custom datasets
• Labelling sound events and their temporal boundaries
• Techniques for balancing datasets and audio augmentation

Building Audio Classification Models

• Applying convolutional neural networks (CNNs) to audio data
• Model inputs: comparing raw waveforms versus extracted features
• Exploring loss functions, evaluation metrics, and preventing overfitting

Event Detection and Temporal Localisation

• Frame-based and segment-based detection strategies
• Post-processing detections via thresholds and smoothing techniques
• Visualising predictions across audio timelines

Advanced Topics and Real-Time Processing

• Applying transfer learning in low-data scenarios
• Model deployment using TensorFlow Lite or ONNX
• Streamlining audio processing and managing latency considerations

Project Development and Application Scenarios

• Designing a comprehensive pipeline from 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