Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

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

Audio Data and Feature Extraction

• Overview of 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

• Utilizing UrbanSound8K, ESC-50, and custom datasets.
• Labeling sound events along with their temporal boundaries.
• Strategies for dataset balancing and audio augmentation.

Building Audio Classification Models

• Applying convolutional neural networks (CNNs) to audio data.
• Input variations: raw waveforms versus extracted features.
• Selection of loss functions, evaluation metrics, and managing overfitting.

Event Detection and Temporal Localization

• Implementing frame-based and segment-based detection strategies.
• Refining detections through thresholding and smoothing techniques.
• Visualizing predictions across audio timelines.

Advanced Topics and Real-Time Processing

• Leveraging transfer learning for scenarios with limited data.
• Model deployment using TensorFlow Lite or ONNX.
• Managing streaming audio processing and latency constraints.

Project Development and Application Scenarios

• Designing an end-to-end pipeline from data ingestion to classification.
• Developing proof-of-concept solutions for surveillance, quality control, or monitoring.
• Integrating logging, alerting, and dashboard or API connectivity.

Summary and Next Steps