AI-Powered Audio Enhancement and Noise Reduction Training Course

Core Concepts of Audio and Noise

• Essential terms: waveform, frequency, amplitude, and dynamic range
• Categories of noise: environmental, equipment-related, and digital artifacts
• Comparison between traditional and AI-powered noise reduction methods

Introduction to AI-Based Audio Enhancement Tools

• The mechanism by which AI models process and refine audio
• Comparative analysis 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 for Windows and macOS
• Integration with platforms such as Zoom, Teams, and Skype
• Conducting live audio tests and resolving frequent technical issues

Refining Recordings using Adobe Enhance

• Uploading and processing podcast-style audio recordings
• Understanding limitations, managing latency, and ensuring quality control
• Synergizing with Adobe Audition or Premiere

Integrating RNNoise into Custom Pipelines

• An overview of the RNNoise open-source library
• Compiling and utilizing RNNoise in conjunction with FFmpeg
• Custom integrations for surveillance or VoIP systems

Assessing Quality and Performance

• Key metrics: signal-to-noise ratio, latency, and CPU/GPU resource usage
• Testing across various use cases: meetings, recordings, and field audio
• Comparing human perception with objective scoring tools

Case Studies and Workflow Integration

• Setting up enterprise conferencing solutions for legal and financial sectors
• Applying noise reduction in media production pipelines
• Cleaning audio for evidence and surveillance review

Recap and Future Steps