AI-Powered Audio Enhancement and Noise Reduction Training Course

Basics of Audio and Noise

• Core concepts: waveform, frequency, amplitude, and dynamic range
• Noise categories: environmental, hardware-related, and digital artifacts
• Conventional methods versus AI-driven noise reduction techniques

Introduction to AI-Based Audio Refinement Utilities

• How AI models analyze and purify audio streams
• Comparative analysis of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment modes: local, cloud-based, and real-time integration

Leveraging Krisp for Live Conferencing

• Setup and configuration on Windows/macOS platforms
• Connecting with Zoom, Teams, and Skype
• Conducting live audio trials and resolving frequent issues

Refining Recordings with Adobe Enhance

• Processing and cleaning podcast-style audio files
• Addressing limitations, latency, and quality oversight
• Synergizing with Adobe Audition or Premiere

Implementing RNNoise in Custom Pipelines

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

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU load
• Evaluating performance across scenarios: meetings, recordings, and field audio
• Comparing human perception against objective scoring utilities

Case Studies and Workflow Integration

• Configuring enterprise conferencing for the legal and financial sectors
• Noise suppression within media production workflows
• Audio refinement for evidentiary and surveillance reviews

Recap and Subsequent Steps