Audio Classification and Event Detection with ML Training Course

Core Principles of Audio Classification

• Categorizing sound events: environmental, mechanical, and human-produced
• Exploring use cases: surveillance, monitoring, and automation
• Distinguishing between audio classification, detection, and segmentation

Audio Data Handling and Feature Extraction

• Understanding various audio file types and formats
• Considering sampling rates, windowing, and frame size parameters
• Extracting features such as MFCCs, chroma, and mel-spectrograms

Data Preparation and Annotation Strategies

• Utilizing datasets like UrbanSound8K, ESC-50, and custom collections
• Annotating sound events and defining temporal boundaries
• Techniques for dataset balancing and audio augmentation

Developing Audio Classification Models

• Applying convolutional neural networks (CNNs) to audio data
• Evaluating input types: raw waveforms versus extracted features
• Selecting loss functions, evaluation metrics, and managing overfitting

Event Detection and Temporal Precision

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

Advanced Techniques and Real-Time Capabilities

• Utilizing transfer learning for scenarios with limited data
• Model deployment using TensorFlow Lite or ONNX
• Handling streaming audio and optimizing for low latency

Project Development and Practical Applications

• 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

Conclusion and Future Pathways