- 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
Audio Classification and Event Detection with ML is a technical course focused on building machine learning models to classify audio and detect sound events in real-world environments.
This instructor-led, live training (online or onsite) is aimed at intermediate-level to advanced-level data professionals who wish to apply machine learning techniques to analyze and classify audio data for use in public safety, manufacturing, smart cities, and multimedia analytics.
By the end of this training, participants will be able to:
- Understand how sound events are modeled and categorized using ML.
- Preprocess audio data using feature extraction techniques like MFCC and spectrograms.
- Build, train, and evaluate models for audio classification and event detection.
- Deploy ML models for real-time or batch-based audio processing in enterprise or embedded settings.
Format of the Course
- Interactive lecture and discussion.
- Lots of exercises and practice.
- Hands-on implementation in a live-lab environment.
Course Customization Options
- To request a customized training for this course, please contact us to arrange.
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
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