AI-Powered Audio Enhancement and Noise Reduction Training Course

Audio and Noise Basics

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

Introduction to AI Audio Enhancement Tools

• Mechanisms by which AI models process and clean audio streams
• Tool evaluation: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local execution, cloud services, and real-time integration

Applying Krisp for Real-Time Conferencing

• Setup and installation on Windows and macOS platforms
• Connecting with Zoom, Teams, and Skype applications
• Conducting live audio tests and resolving common technical issues

Improving Recordings Using Adobe Enhance

• Processing and cleaning podcast-style audio files
• Managing limitations, latency, and quality control measures
• Combining workflows with Adobe Audition or Premiere

Implementing RNNoise in Custom Pipelines

• Understanding 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 load
• Testing across various scenarios: meetings, studio recordings, and field audio
• Comparing human perception with objective scoring tools

Case Studies and Workflow Integration

• Configuring enterprise conferencing systems for legal and finance sectors
• Implementing noise reduction within media production pipelines
• Audio refinement for evidence review and surveillance analysis

Conclusion and Future Steps