AI-Powered Audio Enhancement and Noise Reduction Training Course

Foundations of Audio and Noise

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

Introduction to AI-Based Audio Enhancement Tools

• Mechanisms by which AI models process and purify audio.
• Comparative analysis of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice.
• Deployment models: local execution, cloud services, and real-time integration.

Applying Krisp in Real-Time Conferencing

• Installation and configuration for Windows and macOS.
• Integrating with Zoom, Teams, and Skype.
• Conducting live audio tests and resolving common issues.

Improving Recordings via Adobe Enhance

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

Implementing RNNoise in Custom Pipelines

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

Assessing Quality and Performance

• Metrics: signal-to-noise ratio, latency, and CPU/GPU resource impact.
• Testing across various scenarios: meetings, recordings, and field audio.
• Comparing human perception with objective scoring tools.

Case Studies and Workflow Integration

• Enterprise conferencing setups for the legal and financial sectors.
• Noise reduction techniques within media production pipelines.
• Audio cleanup for evidence review and surveillance analysis.

Summary and Path Forward