Audio Classification and Event Detection with ML Training Course

Audio Classification Fundamentals

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

Audio Data Handling 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 Labeling

• Utilizing datasets like UrbanSound8K, ESC-50, and custom collections.
• Labeling specific sound events and their temporal boundaries.
• Techniques for balancing datasets and audio augmentation.

Constructing Audio Classification Models

• Applying Convolutional Neural Networks (CNNs) to audio data.
• Evaluating model inputs: raw waveforms versus pre-extracted features.
• Selection of loss functions, evaluation metrics, and mitigation of overfitting.

Event Detection and Temporal Localization

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

Advanced Concepts and Real-Time Processing

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

Project Development and Application Contexts

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

Conclusion and Further Steps