Audio Classification and Event Detection with ML Training Course

Foundations of Audio Classification

• Categorizing sound events: environmental, mechanical, and human-generated
• Overview of applications: 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
• Extracting MFCCs, chroma features, and mel-spectrograms

Data Preparation and Annotation

• Utilizing datasets such as UrbanSound8K, ESC-50, and custom collections
• Annotating sound events and their temporal boundaries
• Techniques for balancing datasets and augmenting audio

Building Audio Classification Models

• Application of convolutional neural networks (CNNs) to audio data
• Input strategies: raw waveforms versus extracted features
• Selecting 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 predictions across audio timelines

Advanced Topics and Real-Time Processing

• Implementing transfer learning in low-data scenarios
• Model deployment using TensorFlow Lite or ONNX
• Managing streaming audio processing and latency requirements

Project Development and Application Scenarios

• Architecting a complete pipeline from data ingestion to classification
• Creating proof-of-concepts for surveillance, quality control, or monitoring systems
• Integrating logging, alerting, and dashboard or API connections

Summary and Next Steps