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Course Outline
Foundations of Audio Classification
- Categories of sound events: environmental, mechanical, and human-generated
- Overview of use cases: surveillance, monitoring, and automation
- Distinguishing between audio classification, detection, and segmentation
Audio Data and Feature Extraction
- Various types of audio files and formats
- Key considerations: sampling rate, windowing, and frame size
- Extracting features such as MFCCs, chroma features, and mel-spectrograms
Data Preparation and Annotation
- Use of standard datasets like UrbanSound8K and ESC-50, alongside custom datasets
- Labeling sound events and defining temporal boundaries
- Techniques for balancing datasets and audio augmentation
Building Audio Classification Models
- Application of Convolutional Neural Networks (CNNs) for audio tasks
- Input types: raw waveforms versus extracted features
- Management of loss functions, evaluation metrics, and overfitting
Event Detection and Temporal Localization
- Strategies for frame-based and segment-based detection
- Refining detections through thresholds and smoothing techniques
- Visualizing predictions on audio timelines
Advanced Topics and Real-Time Processing
- Utilizing transfer learning for scenarios with limited data
- Deploying models using TensorFlow Lite or ONNX
- Considerations for streaming audio processing and latency
Project Development and Application Scenarios
- Designing an end-to-end pipeline from ingestion to classification
- Developing proof-of-concept solutions for surveillance, quality control, or monitoring
- Implementing logging, alerting, and integration with dashboards or APIs
Summary and Next Steps
Requirements
- A solid understanding of machine learning concepts and model training processes
- Proficiency in Python programming and data preprocessing techniques
- Familiarity with the fundamentals of digital audio
Intended Audience
- Data scientists
- Machine learning engineers
- Researchers and developers specializing in audio signal processing
21 Hours
