Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
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
- Utilising 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) for audio analysis.
- Model inputs: raw waveforms versus features.
- Loss functions, evaluation metrics, and managing overfitting.
Event Detection and Temporal Localisation
- Strategies for frame-based and segment-based detection.
- Post-processing detections using thresholds and smoothing techniques.
- Visualising predictions on audio timelines.
Advanced Topics and Real-Time Processing
- Transfer learning for scenarios with limited data.
- Deploying models using TensorFlow Lite or ONNX.
- Streaming audio processing and considerations for latency.
Project Development and Application Scenarios
- Designing a full 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 pre-processing.
- Familiarity with the fundamentals of digital audio.
Audience
- Data scientists.
- Machine learning engineers.
- Researchers and developers specialising in audio signal processing.
21 Hours
