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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

  • Types of audio files and their formats.
  • Considerations for sampling rate, windowing, and frame size.
  • Extraction of MFCCs, chroma features, and mel-spectrograms.

Data Preparation and Annotation

  • Working with UrbanSound8K, ESC-50, and custom datasets.
  • Annotating sound events and defining temporal boundaries.
  • Balancing datasets and applying audio augmentation techniques.

Building Audio Classification Models

  • Utilizing convolutional neural networks (CNNs) for audio data.
  • Evaluating model inputs: raw waveforms versus extracted features.
  • Selecting loss functions, evaluation metrics, and managing overfitting.

Event Detection and Temporal Localization

  • Strategies for frame-based and segment-based detection.
  • Post-processing detections using thresholds and smoothing techniques.
  • Visualizing predictions along audio timelines.

Advanced Topics and Real-Time Processing

  • Applying transfer learning in low-data scenarios.
  • Deploying models via TensorFlow Lite or ONNX.
  • Handling streaming audio processing and managing latency.

Project Development and Application Scenarios

  • Designing a complete pipeline from ingestion to classification.
  • Developing proof-of-concept solutions for surveillance, quality control, or monitoring.
  • Implementing logging, alerting systems, and integration with dashboards or APIs.

Summary and Next Steps

Requirements

  • A solid understanding of machine learning concepts and the model training process.
  • Proficiency in Python programming and data preprocessing techniques.
  • Familiarity with the fundamentals of digital audio.

Target Audience

  • Data scientists.
  • Machine learning engineers.
  • Researchers and developers specializing in audio signal processing.
 21 Hours

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  • Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
  • Flexible Schedule: Dates and times adapted to your team's agenda.
  • Format: Online (live), In-company (at your offices), or Hybrid.
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