AI-Powered Audio Enhancement and Noise Reduction Training Course

Fundamentals of Audio and Noise

• Key concepts: waveform, frequency, amplitude, and dynamic range
• Types of noise: environmental, equipment, and digital artifacts
• Conventional versus AI-driven noise mitigation approaches

Overview of AI-Based Audio Refinement Tools

• How AI models process and clean audio
• Tool comparison: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment options: local, cloud, and real-time integration

Utilising Krisp for Real-Time Conferencing

• Installation and setup on Windows/macOS
• Integration with Zoom, Teams, and Skype
• Live audio testing and troubleshooting common issues

Enhancing Recordings with Adobe Enhance

• Uploading and refining podcast-style recordings
• Limitations, latency, and quality control
• Using in conjunction with Adobe Audition or Premiere

Implementing RNNoise in Custom Pipelines

• Overview of the RNNoise open-source library
• Compiling and using RNNoise with FFmpeg
• Custom integrations in surveillance or VoIP systems

Evaluating Quality and Performance

• Metrics: signal-to-noise ratio, latency, and CPU/GPU impact
• Testing across use cases: meetings, recordings, and field audio
• Human perception versus objective scoring tools

Case Studies and Workflow Integration

• Enterprise conferencing setup for legal and finance sectors
• Noise mitigation in media production pipelines
• Audio cleaning for evidence and surveillance review

Summary and Next Steps