Audio Classification and Event Detection with ML Training Course

Core Concepts of Audio Classification

• Categorizing sound events: environmental, mechanical, and human-generated.
• Key use cases including surveillance, monitoring, and automation.
• Distinguishing between audio classification, detection, and segmentation.

Handling Audio Data and Feature Extraction

• Varieties of audio file types and formats.
• Considerations for sampling rates, windowing, and frame sizes.
• Techniques for extracting MFCCs, chroma features, and mel-spectrograms.

Data Preparation and Annotation Strategies

• Utilizing UrbanSound8K, ESC-50, and bespoke datasets.
• Labeling sound events and defining temporal boundaries.
• Methods for balancing datasets and audio augmentation.

Constructing Audio Classification Models

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

Event Detection and Temporal Localization

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

Advanced Applications and Real-Time Processing

• Transfer learning techniques for data-scarce environments.
• Model deployment using TensorFlow Lite or ONNX.
• Streaming audio processing and latency management.

Project Development and Practical Applications

• Architecting complete pipelines from data ingestion to classification.
• Creating proof-of-concept solutions for surveillance, quality control, or monitoring.
• Implementing logging, alerting systems, and integrations with dashboards or APIs.

Conclusion and Future Pathways