Audio Classification and Event Detection with ML Training Course

Core Principles of Audio Classification

• Sound event categories: environmental, mechanical, and human-originated
• Survey of applications: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data Handling and Feature Extraction

• Various 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 Strategies

• Utilizing UrbanSound8K, ESC-50, and custom datasets
• Annotating sound events and their temporal boundaries
• Dataset balancing and audio augmentation techniques

Constructing Audio Classification Models

• Applying convolutional neural networks (CNNs) to audio data
• Model inputs: raw waveforms versus extracted features
• Loss functions, evaluation metrics, and managing overfitting

Event Detection and Temporal Localization

• Frame-based and segment-based detection methodologies
• Post-processing detections via thresholds and smoothing
• Visualizing predictions across audio timelines

Advanced Concepts and Real-Time Processing

• Transfer learning for scenarios with limited data
• Model deployment using TensorFlow Lite or ONNX
• Streaming audio processing and latency management

Project Development and Real-World Applications

• Designing end-to-end pipelines: from ingestion to classification
• Building proof-of-concepts for surveillance, quality control, or monitoring
• Implementing logging, alerting, and integration with dashboards or APIs

Wrap-Up and Future Directions