AI-Powered Audio Enhancement and Noise Reduction Training Course

Core Concepts of Audio and Noise

• Essential principles: waveforms, frequency, amplitude, and dynamic range
• Noise categories: ambient sounds, hardware issues, and digital distortions
• Comparing conventional noise reduction techniques with AI-based methods

Introduction to AI-Powered Audio Tools

• Mechanisms by which AI models process and refine audio signals
• Comparative analysis of Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local processing, cloud services, and real-time integration

Utilizing Krisp for Live Meetings

• Setup and configuration for Windows and macOS
• Connecting with platforms like Zoom, Teams, and Skype
• Conducting live audio tests and resolving typical technical issues

Refining Recordings via Adobe Enhance

• Processing and cleaning podcast-style audio files
• Managing constraints, latency, and ensuring output quality
• Utilizing alongside Adobe Audition or Premiere Pro

Implementing RNNoise in Custom Workflows

• Insight into the RNNoise open-source library
• Building and integrating RNNoise with FFmpeg
• Custom applications in surveillance or VoIP systems

Assessing Quality and System Performance

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

Real-World Scenarios and Workflow Adoption

• Configuring enterprise conferencing for legal and financial sectors
• Applying noise suppression in media production pipelines
• Enhancing audio clarity for evidence and surveillance review

Conclusion and Future Directions