AI-Powered Audio Enhancement and Noise Reduction Training Course

Foundational Concepts: Audio & Noise

• Essential elements: waveforms, frequency, amplitude, and dynamic range
• Identifying noise types: environmental, equipment-related, and digital artifacts
• Comparing traditional methods with AI-driven noise reduction strategies

AI-Based Audio Enhancement Tools: An Overview

• The mechanics of how AI models process and refine audio
• Comparing popular tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment models: local processing, cloud-based solutions, and real-time integration

Real-Time Conferencing with Krisp

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

Refining Recordings via Adobe Enhance

• Processing and cleaning podcast-style audio recordings
• Managing limitations, latency, and ensuring quality control
• Leveraging alongside Adobe Audition or Premiere

Implementing RNNoise in Custom Pipelines

• Introduction to the RNNoise open-source library
• Compiling and applying RNNoise using FFmpeg
• Building custom integrations for surveillance or VoIP systems

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU load
• Performance testing across scenarios: meetings, recorded content, and field audio
• Balancing human perception with objective scoring tools

Case Studies & Workflow Integration

• Enterprise conferencing configurations for legal and financial sectors
• Applying noise reduction within media production workflows
• Audio refinement for evidence review and surveillance analysis

Summary & Future Steps