AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Fundamentals

• Core concepts: waveform, frequency, amplitude, and dynamic range
• Noise categories: environmental, equipment-related, and digital artifacts
• Contrast between conventional and AI-powered noise reduction techniques

Introduction to AI-Based Audio Enhancement Tools

• Mechanisms by which AI models process and purify audio
• Tool analysis: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local, cloud-based, and real-time integration

Leveraging Krisp for Real-Time Conferencing

• Setup and installation on Windows and macOS platforms
• Compatibility with Zoom, Teams, and Skype
• Live audio testing and resolving common operational issues

Improving Recordings via Adobe Enhance

• Processing and refining podcast-style recordings
• Understanding constraints, latency, and quality oversight
• Combining with Adobe Audition or Premiere for enhanced results

Implementing RNNoise in Custom Pipelines

• Overview of the RNNoise open-source library
• Building and utilizing RNNoise alongside FFmpeg
• Custom integration within surveillance or VoIP systems

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU load
• Testing across scenarios: meetings, recordings, and field audio
• Comparing human perception with objective scoring tools

Case Studies and Workflow Integration

• Enterprise conferencing configurations for legal and financial sectors
• Noise mitigation within media production pipelines
• Audio refinement for evidence and surveillance analysis

Recap and Future Directions