AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Essentials

• Core concepts: waveforms, frequency, amplitude, and dynamic range
• Noise categories: environmental, hardware-related, and digital artifacts
• Comparative analysis of traditional versus AI-driven noise reduction methods

Survey of AI-Powered Audio Enhancement Solutions

• Mechanics of AI model audio processing and cleaning
• Platform comparison: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local, cloud-based, and real-time integration

Implementing Krisp for Live Conferencing

• Setup and configuration on Windows and macOS
• Connectivity with Zoom, Teams, and Skype
• Real-time audio testing and resolution of common issues

Polishing Recordings with Adobe Enhance

• Processing and cleansing podcast-style audio files
• Addressing constraints, latency, and quality assurance
• Synergistic use with Adobe Audition or Premiere

Integrating RNNoise into Custom Workflows

• Introduction to the RNNoise open-source library
• Compilation and application of RNNoise alongside FFmpeg
• Tailored integrations for surveillance or VoIP systems

Assessing Quality and System Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU load
• Cross-platform testing: meetings, recorded media, and field audio
• Contrasting human perception with objective scoring mechanisms

Case Studies and Process Integration

• Configuring enterprise conferencing for legal and financial industries
• Applying noise reduction within media production chains
• Audio purification for evidentiary and surveillance analysis

Conclusion and Future Directions