AI-Powered Audio Enhancement and Noise Reduction Training Course

Basics of Audio and Noise

• Core ideas: waveform, frequency, amplitude, and dynamic range
• Noise categories: environmental, hardware-related, and digital artifacts
• Contrasting conventional methods with AI-driven noise reduction techniques

Introduction to AI-Based Audio Optimization Tools

• The mechanism by which AI models process and purify audio
• Comparative analysis of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: on-premise, cloud-based, and real-time integration

Utilizing Krisp for Live Meetings

• Installing and configuring on Windows/macOS systems
• Connecting with Zoom, Teams, and Skype
• Conducting live audio tests and resolving frequent issues

Improving Recordings via Adobe Enhance

• Processing and cleaning podcast-style recordings
• Understanding constraints, latency, and maintaining quality
• Integrating with Adobe Audition or Premiere

Implementing RNNoise in Custom Workflows

• An introduction to the RNNoise open-source library
• Compiling and utilizing RNNoise alongside FFmpeg
• Custom integrations for surveillance or VoIP systems

Assessing Quality and Efficiency

• Key indicators: signal-to-noise ratio, latency, and CPU/GPU load
• Testing across various applications: meetings, recordings, and field audio
• Comparing human perception with objective scoring utilities

Real-World Examples and Workflow Incorporation

• Setting up enterprise conferencing for legal and financial industries
• Applying noise reduction within media production pipelines
• Cleaning audio for evidence and surveillance analysis

Recap and Future Directions