AI-Powered Audio Enhancement and Noise Reduction Training Course

Basics of Audio and Noise

• Core concepts: waveforms, frequency, amplitude, and dynamic range
• Categories of noise: environmental, equipment-related, and digital artifacts
• Comparing traditional methods versus AI-driven noise suppression techniques

Introduction to AI-Based Audio Enhancement Platforms

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

Implementing Krisp for Real-Time Conferencing

• Setting up and installing on Windows/macOS
• Seamless integration with Zoom, Teams, and Skype
• Conducting live audio trials and resolving common technical issues

Improving Recordings via Adobe Enhance

• Processing and refining podcast-style recordings
• Addressing limitations, latency issues, and maintaining quality control
• Collaborative use with Adobe Audition or Premiere

Integrating RNNoise into Custom Workflows

• Introduction to the RNNoise open-source library
• Compiling and applying RNNoise alongside FFmpeg
• Building custom integrations for surveillance or VoIP systems

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU resource usage
• Evaluating performance across various scenarios: meetings, recordings, and field audio
• Balancing human perception with objective scoring instruments

Real-World Case Studies and Workflow Integration

• Configuring enterprise conferencing solutions for legal and financial sectors
• Applying noise reduction within media production pipelines
• Refining audio for evidence analysis and surveillance reviews

Recap and Future Steps