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

• Varieties 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 datasets such as UrbanSound8K, ESC-50, and custom corpora
• Annotating sound events and defining temporal boundaries
• Techniques for balancing datasets and augmenting audio data

Constructing Audio Classification Models

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

Event Detection and Temporal Localization

• Strategies for frame-based and segment-based detection
• Refining detections through thresholding and smoothing techniques
• Visualizing predictive outputs on audio timelines

Advanced Topics and Real-Time Processing

• Leveraging transfer learning in low-data scenarios
• Model deployment via TensorFlow Lite or ONNX
• Streaming audio processing and managing latency constraints

Project Development and Application Scenarios

• Architecting a complete pipeline from data ingestion to classification
• Developing proof-of-concept solutions for surveillance, quality control, or monitoring
• Integrating logging, alerting systems, and dashboard or API connectivity

Summary and Next Steps