AI-Powered Audio Enhancement and Noise Reduction Training Course

Essentials of Audio and Noise

• Core concepts: waveforms, frequency, amplitude, and dynamic range.
• Noise categories: environmental, equipment-related, and digital artifacts.
• Comparing traditional methods with AI-driven noise reduction techniques.

Introduction to AI-Based Audio Enhancement Tools

• How AI models process and refine audio signals.
• Comparative analysis of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice.
• Deployment strategies: local, cloud-based, and real-time integration options.

Leveraging Krisp for Live Conferencing

• Setting up and installing on Windows/macOS platforms.
• Integrating with Zoom, Teams, and Skype.
• Conducting live audio tests and resolving common issues.

Improving Recordings using Adobe Enhance

• Processing and cleaning podcast-style recordings.
• Understanding constraints, latency factors, and quality assurance.
• Utilizing alongside Adobe Audition or Premiere.

Implementing RNNoise in Custom Workflows

• Introduction to the RNNoise open-source library.
• Compiling and integrating RNNoise with FFmpeg.
• Custom applications in surveillance or VoIP systems.

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU load.
• Testing across various scenarios: meetings, studio recordings, and field audio.
• Balancing human perception with objective scoring tools.

Case Studies and Workflow Integration

• Enterprise conferencing configurations for the legal and financial sectors.
• Applying noise reduction in media production pipelines.
• Audio cleanup for evidence review and surveillance operations.

Recap and Future Directions