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Course Outline
Foundations of Audio Classification for Government
- Sound event types: environmental, mechanical, human-generated
- Overview of use cases: surveillance, monitoring, automation
- Audio classification vs detection vs segmentation
- Types of audio files and formats
- Sampling rate, windowing, frame size considerations
- Extracting MFCCs, chroma features, mel-spectrograms
Data Preparation and Annotation for Government
- UrbanSound8K, ESC-50, and custom datasets
- Labeling sound events and temporal boundaries
- Balancing datasets and augmenting audio
Building Audio Classification Models for Government
- Using convolutional neural networks (CNNs) for audio
- Model input: raw waveform vs features
- Loss functions, evaluation metrics, and overfitting
Event Detection and Temporal Localization for Government
- Frame-based and segment-based detection strategies
- Post-processing detections using thresholds and smoothing
- Visualizing predictions on audio timelines
Advanced Topics and Real-Time Processing for Government
- Transfer learning for low-data scenarios
- Deploying models with TensorFlow Lite or ONNX
- Streaming audio processing and latency considerations
Project Development and Application Scenarios for Government
- Designing a full pipeline: ingestion to classification
- Developing a proof-of-concept for surveillance, quality control, or monitoring
- Logging, alerting, and integration with dashboards or APIs
Summary and Next Steps for Government
Requirements
- An understanding of machine learning concepts and model training for government applications
- Experience with Python programming and data preprocessing techniques
- Familiarity with digital audio fundamentals and their application in governmental projects
Audience
- Data scientists working in public sector roles
- Machine learning engineers supporting government initiatives
- Researchers and developers focused on audio signal processing for government use
21 Hours
