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Course Outline
Foundations of Audio Classification
- Sound event types: environmental, mechanical, human-generated
- Overview of use cases: surveillance, monitoring, automation for government applications
- Audio classification vs detection vs segmentation
Audio Data and Feature Extraction
- Types of audio files and formats used in public sector workflows
- Sampling rate, windowing, frame size considerations to ensure data integrity and accuracy for government use
- Extracting MFCCs, chroma features, mel-spectrograms for robust analysis
Data Preparation and Annotation
- UrbanSound8K, ESC-50, and custom datasets suitable for government projects
- Labeling sound events and temporal boundaries to enhance data quality and reliability
- Balancing datasets and augmenting audio to improve model performance and fairness in government applications
Building Audio Classification Models
- Using convolutional neural networks (CNNs) for audio classification in public sector environments
- Model input: raw waveform vs features, optimizing for efficiency and accuracy for government use cases
- Loss functions, evaluation metrics, and strategies to prevent overfitting in government models
Event Detection and Temporal Localization
- Frame-based and segment-based detection strategies tailored for government applications
- Post-processing detections using thresholds and smoothing techniques to refine results for government use
- Visualizing predictions on audio timelines to facilitate interpretation and decision-making in government operations
Advanced Topics and Real-Time Processing
- Transfer learning for low-data scenarios, enhancing model adaptability for government projects
- Deploying models with TensorFlow Lite or ONNX to support real-time processing in government environments
- Streaming audio processing and latency considerations to ensure timely and effective response in government applications
Project Development and Application Scenarios
- Designing a full pipeline: from data ingestion to classification for comprehensive government solutions
- Developing a proof-of-concept for surveillance, quality control, or monitoring tailored to government needs
- Logging, alerting, and integration with dashboards or APIs to support transparency and accountability in government operations
Summary and Next Steps
Requirements
- An understanding of machine learning concepts and model training for government applications
- Experience with Python programming and data preprocessing for government projects
- Familiarity with digital audio fundamentals for government use cases
Audience
- Data scientists in the public sector
- Machine learning engineers for government agencies
- Researchers and developers specializing in audio signal processing for government initiatives
21 Hours
