AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Fundamentals

• Core concepts: waveform, frequency, amplitude, and dynamic range
• Categories of noise: environmental, hardware-related, and digital artifacts
• Comparison between conventional and AI-driven noise reduction methods

Introduction to AI-Based Audio Enhancement Utilities

• The mechanics of how AI models process and refine audio
• Comparative analysis of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local, cloud-based, and real-time integration options

Leveraging Krisp for Live Conferencing

• Setup and installation procedures on Windows and macOS systems
• Compatibility and integration with Zoom, Teams, and Skype
• Conducting live audio trials and resolving typical operational issues

Refining Recordings with Adobe Enhance

• Uploading and processing podcast-style audio files
• Addressing limitations, latency considerations, and quality management
• Utilizing the tool alongside Adobe Audition or Premiere

Implementing RNNoise in Custom Workflows

• An overview of the RNNoise open-source library
• Compiling and utilizing RNNoise in conjunction with FFmpeg
• Developing custom integrations for surveillance systems or VoIP infrastructure

Assessing Quality and Performance Metrics

• Key indicators: signal-to-noise ratio, latency, and CPU/GPU load
• Evaluating performance across various scenarios: meetings, recordings, and field audio
• Comparing human auditory perception with objective scoring methodologies

Case Studies and Workflow Integration

• Configuring enterprise conferencing solutions for the legal and financial sectors
• Applying noise reduction in media production pipelines
• Audio refinement for evidentiary review and surveillance analysis

Recap and Future Directions