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Course Outline
Foundations of Audio Classification
- Types of sound events: environmental, mechanical, and human-generated.
- Overview of use cases: surveillance, monitoring, and automation.
- Differences between audio classification, detection, and segmentation.
Audio Data and Feature Extraction
- Types of audio files and formats.
- Considerations for sampling rate, windowing, and frame size.
- Extracting MFCCs, chroma features, and mel-spectrograms.
Data Preparation and Annotation
- Utilizing datasets such as UrbanSound8K, ESC-50, and custom datasets.
- Labeling sound events and defining temporal boundaries.
- Balancing datasets and augmenting audio data.
Building Audio Classification Models
- Applying convolutional neural networks (CNNs) to audio data.
- Model inputs: comparing raw waveforms versus extracted features.
- Understanding loss functions, evaluation metrics, and strategies to prevent overfitting.
Event Detection and Temporal Localization
- Strategies for frame-based and segment-based detection.
- Post-processing detections using thresholds and smoothing techniques.
- Visualizing predictions on audio timelines.
Advanced Topics and Real-Time Processing
- Leveraging transfer learning for low-data scenarios.
- Deploying models using TensorFlow Lite or ONNX.
- Handling streaming audio processing and latency considerations.
Project Development and Application Scenarios
- Designing a comprehensive pipeline from ingestion to classification.
- Developing a proof-of-concept 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.
- Experience with Python programming and data preprocessing.
- Familiarity with the fundamentals of digital audio.
Audience
- Data scientists.
- Machine learning engineers.
- Researchers and developers specializing in audio signal processing.
21 Hours
