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

• Variations in 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

• Utilization of UrbanSound8K, ESC-50, and custom datasets.
• Labeling of sound events and their temporal boundaries.
• Strategies for balancing datasets and audio augmentation.

Developing Audio Classification Models

• Application of convolutional neural networks (CNNs) for audio analysis.
• Model input options: raw waveform 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 utilizing thresholds and smoothing techniques.
• Visualization of predictions across audio timelines.

Advanced Topics and Real-Time Processing

• Application of transfer learning in scenarios with limited data.
• Deployment of models using TensorFlow Lite or ONNX.
• Streaming audio processing and evaluation of latency considerations.

Project Development and Application Scenarios

• Designing a comprehensive pipeline from data ingestion to classification.
• Creating a proof-of-concept for surveillance, quality control, or monitoring purposes.
• Integration of logging, alerting systems, and dashboards or APIs.

Summary and Next Steps