AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Basics

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

Introduction to AI Audio Enhancement Solutions

• How AI models process and purify audio signals
• Tool comparison: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local setups, cloud services, and real-time integrations

Leveraging Krisp for Live Conferencing

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

Refining Recordings with Adobe Enhance

• Processing and cleaning podcast-style audio files
• Understanding limitations, latency considerations, and quality control
• Combining with Adobe Audition or Premiere for optimal results

Implementing RNNoise in Custom Systems

• An overview of the RNNoise open-source library
• Compiling and integrating RNNoise with FFmpeg
• Custom applications in surveillance or VoIP architectures

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU resource usage
• Testing across various scenarios: meetings, recorded media, and field audio
• Balancing human perception with objective scoring tools

Case Studies and Workflow Integration

• Enterprise conferencing configurations for legal and finance industries
• Applying noise reduction within media production pipelines
• Audio cleanup for evidence review and surveillance monitoring

Wrap-Up and Future Directions