AI-Powered Audio Enhancement and Noise Reduction Training Course

Basics of Sound and Noise

• Core concepts: waveform, frequency, amplitude, and dynamic range
• Comparison between conventional and AI-based noise reduction techniques

Introduction to AI-Driven Audio Enhancement Tools

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

Leveraging Krisp for Live Conferencing

• Setup and installation on Windows/macOS platforms
• Compatibility with Zoom, Teams, and Skype
• Live audio testing and resolving frequent issues

Improving Recordings via Adobe Enhance

• Processing and cleaning podcast-style audio files
• Understanding constraints, latency, and quality management
• Combining with Adobe Audition or Premiere Pro

Integrating RNNoise into Custom Workflows

• Overview of the RNNoise open-source library
• Compilation and application of RNNoise with FFmpeg
• Custom applications in surveillance or VoIP systems

Assessing Quality and Performance Metrics

• Key metrics: signal-to-noise ratio, latency, CPU/GPU load
• Testing across various scenarios: meetings, recordings, field audio
• Human perception versus objective scoring methods

Case Studies and Workflow Integration

• Enterprise conferencing configurations for legal and financial industries
• Audio refinement for evidence and surveillance analysis

Recap and Future Directions