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Duration 21 hours
Course Outline
Foundational Principles of Audio Classification
- Categories of acoustic phenomena: environmental, mechanical, and anthropogenic
- Application domains: surveillance, operational monitoring, and process automation
- Distinguishing audio classification from detection and segmentation
Audio Data Management and Feature Extraction
- Standard audio file structures and formats
- Parameters for sampling rate, windowing, and frame size
- Derivation of MFCCs, chroma features, and mel-spectrograms
Data Preparation and Annotation Protocols
- Utilizing UrbanSound8K, ESC-50, and proprietary datasets
- Labeling acoustic events and temporal boundaries
- Dataset balancing and audio augmentation techniques
Development of Audio Classification Models
- Application of convolutional neural networks (CNNs) for audio analysis
- Model input specifications: raw waveforms versus engineered features
- Loss functions, evaluation metrics, and mitigation of overfitting
Event Detection and Temporal Localization
- Frame-based and segment-based detection methodologies
- Post-processing techniques using thresholds and smoothing
- Visualization of predictions along audio timelines
Advanced Methodologies and Real-Time Processing
- Transfer learning for data-scarce environments
- Model deployment via TensorFlow Lite or ONNX
- Streaming audio processing and latency optimization
Project Implementation and Government Application Scenarios
- Architecting end-to-end pipelines from ingestion to classification
- Proof-of-concept development for surveillance, quality assurance, and monitoring
- Logging, alerting mechanisms, and integration with dashboards or APIs
Conclusion and Strategic Next Steps
Requirements
- Proficiency in machine learning concepts and model training
- Experience with Python programming and data preprocessing
- Familiarity with digital audio fundamentals
Target Audience
- Data scientists
- Machine learning engineers
- Researchers and developers in audio signal processing
