AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Fundamentals

• Core concepts: waveform, frequency, amplitude, and dynamic range
• Noise categories: environmental, equipment-related, and digital artifacts
• Comparison between conventional and AI-based noise reduction methods

Introduction to AI-Based Audio Enhancement Utilities

• How AI models process and purify audio signals
• Tool evaluation: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local, cloud-based, and real-time integration

Leveraging Krisp for Real-Time Conferencing

• Setup and configuration on Windows/macOS platforms
• Compatibility with Zoom, Teams, and Skype
• Conducting live audio tests and resolving typical issues

Improving Recordings via Adobe Enhance

• Processing and cleaning podcast-style audio files
• Understanding constraints, latency, and quality assurance
• Combining with Adobe Audition or Premiere for enhanced results

Implementing RNNoise in Custom Workflows

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

Assessing Quality and Performance

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

Case Studies and Workflow Implementation

• Enterprise conferencing configurations for legal and financial industries
• Noise suppression in media production pipelines
• Audio purification for evidentiary and surveillance review

Conclusion and Future Directions