AI-Powered Audio Enhancement and Noise Reduction Training Course

Core Principles of Sound and Interference

• Fundamental concepts: waveform, frequency, amplitude, and dynamic range
• Noise categories: environmental, hardware-related, and digital artifacts
• Comparing conventional methods with AI-based noise reduction techniques

Introduction to AI-Driven Audio Improvement Utilities

• Mechanisms by which AI models process and refine sound
• Comparison of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local, cloud-based, and real-time integration

Applying Krisp to Live Video Meetings

• Setup and configuration on Windows and macOS systems
• Connecting with Zoom, Microsoft Teams, and Skype
• Conducting live sound tests and resolving common technical issues

Refining Recordings via Adobe Enhance

• Processing and cleaning podcast-style audio files
• Understanding limitations, latency constraints, and quality oversight
• Utilizing alongside Adobe Audition or Premiere

Implementing RNNoise in Bespoke Systems

• Insight into the RNNoise open-source library
• Building and applying RNNoise using FFmpeg
• Custom integrations within surveillance or VoIP infrastructures

Assessing Quality and Operational Efficiency

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

Practical Examples and Workflow Integration

• Setting up enterprise conferencing for legal and financial industries
• Implementing noise reduction in media production workflows
• Cleaning audio for evidentiary analysis and surveillance review

Recap and Future Directions