AI-Powered Audio Enhancement and Noise Reduction Training Course

Fundamentals of Audio and Noise

• Core concepts: waveforms, frequency, amplitude, and dynamic range.
• Identifying noise types: environmental, equipment-related, and digital artifacts.
• Contrasting traditional methods with AI-driven noise reduction techniques.

Exploring AI-Based Audio Enhancement Tools

• Understanding how AI models process and refine audio signals.
• Comparing tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice.
• Reviewing deployment strategies: local, cloud-based, and real-time integrations.

Leveraging Krisp for Real-Time Conferencing

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

Improving Recordings with Adobe Enhance

• Processing and cleaning podcast-style audio recordings.
• Addressing limitations, latency considerations, and quality control.
• Combining with Adobe Audition or Premiere for enhanced results.

Implementing RNNoise in Custom Pipelines

• Introduction to the RNNoise open-source library.
• Compiling and utilizing RNNoise alongside FFmpeg.
• Developing custom integrations for surveillance or VoIP systems.

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU impact.
• Evaluating performance across meetings, recordings, and field audio.
• Balancing human perception with objective scoring tools.

Case Studies and Workflow Integration

• Establishing enterprise conferencing standards for legal and finance sectors.
• Applying noise reduction in media production pipelines.
• Optimizing audio clarity for evidence and surveillance review.

Conclusion and Future Directions