Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Categories of sound events: environmental, mechanical, and human-generated.
• Overview of key 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 rates, windowing, and frame sizes.
• Extraction of MFCCs, chroma features, and mel-spectrograms.

Data Preparation and Annotation

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

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 mitigation of overfitting.

Event Detection and Temporal Localization

• Implementation of frame-based and segment-based detection strategies.
• Post-processing of detections through thresholding and smoothing.
• Visualization of predictions along audio timelines.

Advanced Topics and Real-Time Processing

• Applying transfer learning in low-data scenarios.
• Model deployment using TensorFlow Lite or ONNX.
• Handling streaming audio processing and latency constraints.

Project Development and Application Scenarios

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

Summary and Next Steps