AI-Powered Audio Enhancement and Noise Reduction Training Course

Essentials of Audio and Noise

• Core concepts: waveform, frequency, amplitude, and dynamic range
• Classifying noise: environmental, equipment-related, and digital artifacts
• Contrasting traditional methods with AI-driven noise reduction techniques

Introduction to AI-Based Audio Refinement Tools

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

Leveraging Krisp for Real-Time Conferencing

• Setting up and installing on Windows or macOS
• Integrating with Zoom, Teams, and Skype
• Conducting live audio assessments and resolving common challenges

Improving Recordings with Adobe Enhance

• Uploading and refining podcast-style audio tracks
• Addressing limitations, latency, and quality assurance
• Using alongside Adobe Audition or Premiere

Implementing RNNoise in Custom Pipelines

• An overview of the RNNoise open-source library
• Compiling and utilizing RNNoise with FFmpeg
• Customizing integrations for surveillance or VoIP systems

Assessing Quality and Performance

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

Case Studies and Workflow Integration

• Configuring enterprise conferencing for legal and financial sectors
• Applying noise reduction in media production workflows
• Purifying audio for evidence and surveillance analysis

Recap and Future Steps