AI-Powered Audio Enhancement and Noise Reduction Training Course

Core Concepts of Audio and Noise

• Essential concepts: waveforms, frequency, amplitude, and dynamic range
• Categories of noise: environmental, hardware-related, and digital artifacts
• Comparative analysis of conventional versus AI-based noise reduction methods

Introduction to AI-Based Audio Enhancement Tools

• Mechanisms by which AI models process and refine audio
• Comparative review of tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
• Deployment strategies: local, cloud-based, and real-time integration

Implementing Krisp for Real-Time Conferencing

• Installation and configuration on Windows and macOS
• Integration with Zoom, Teams, and Skype
• Conducting live audio tests and resolving common technical issues

Refining Recordings using Adobe Enhance

• Uploading and processing podcast-style audio files
• Understanding limitations, latency factors, and quality assurance
• Utilization alongside Adobe Audition or Premiere

Integrating RNNoise into Custom Workflows

• An overview of the RNNoise open-source library
• Compilation and usage of RNNoise in conjunction with FFmpeg
• Custom integration within surveillance or VoIP systems

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU load
• Testing across various scenarios: meetings, recorded content, and field audio
• Comparing human perception with objective scoring mechanisms

Case Studies and Workflow Integration

• Setting up enterprise conferencing for legal and financial sectors
• Applying noise reduction in media production pipelines
• Audio refinement for evidentiary and surveillance analysis

Recap and Future Directions