AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Fundamentals

• Core concepts: waveform, frequency, amplitude, and dynamic range.
• Categories of noise: environmental, equipment-related, and digital artifacts.
• Comparison of traditional versus AI-driven noise reduction methodologies.

Overview of AI-Powered Audio Enhancement Solutions

• How AI models process and refine audio signals.
• Comparative analysis of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice.
• Deployment strategies: local, cloud-based, and real-time integration.

Leveraging Krisp for Real-Time Conferencing

• Installation and configuration on Windows and macOS platforms.
• Integration with Zoom, Microsoft Teams, and Skype.
• Conducting live audio tests and troubleshooting common challenges.

Refining Recordings with Adobe Enhance

• Uploading and processing podcast-style recordings for cleaner audio.
• Understanding limitations, latency factors, and quality control mechanisms.
• Utilizing Adobe Enhance alongside Adobe Audition or Premiere.

Implementing RNNoise in Custom Pipelines

• Introduction to the RNNoise open-source library.
• Compiling and utilizing RNNoise with FFmpeg.
• Custom integrations within surveillance or VoIP systems.

Assessing Quality and Performance

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

Case Studies and Workflow Integration

• Enterprise conferencing configurations for the legal and finance sectors.
• Applying noise reduction within media production pipelines.
• Audio cleaning processes for evidence and surveillance review.

Summary and Future Steps