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Course Outline
Foundations of Audio Classification
- Sound event types: environmental, mechanical, human-generated
- Overview of use cases: surveillance, monitoring, automation
- Audio classification vs detection vs segmentation
Audio Data and Feature Extraction
- Types of audio files and formats
- Sampling rate, windowing, frame size considerations
- Extracting MFCCs, chroma features, mel-spectrograms
Data Preparation and Annotation
- UrbanSound8K, ESC-50, and custom datasets
- Labeling sound events and temporal boundaries
- Balancing datasets and augmenting audio
Building Audio Classification Models
- Using convolutional neural networks (CNNs) for audio
- Model input: raw waveform vs features
- Loss functions, evaluation metrics, and overfitting
Event Detection and Temporal Localization
- 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
- Transfer learning for low-data scenarios
- Deploying models with TensorFlow Lite or ONNX
- Streaming audio processing and latency considerations
Project Development and Application Scenarios
- 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
Requirements
- An understanding of machine learning concepts and model training
- Experience with Python programming and data preprocessing
- Familiarity with digital audio fundamentals
Audience
- Data scientists
- Machine learning engineers
- Researchers and developers in audio signal processing
21 Hours
