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Duration 14 hours (2 days)
Course Outline
Core Audio and Noise Concepts
- Fundamental ideas: waveforms, frequency, amplitude, and dynamic range
- Categories of noise: environmental, equipment-related, and digital artifacts
- Comparing conventional methods with AI-driven noise reduction techniques
Introduction to AI-Powered Audio Enhancement Solutions
- How AI models process and refine audio signals
- Comparing tools: Krisp, Adobe Enhance, RNNoise, and NVIDIA RTX Voice
- Deployment models: local, cloud-based, and real-time integration
Utilizing Krisp for Live Conferencing
- Installation and configuration on Windows/macOS systems
- Connecting with Zoom, Teams, and Skype
- Conducting live audio tests and resolving common issues
Improving Recordings with Adobe Enhance
- Processing and cleaning podcast-style audio recordings
- Understanding constraints, latency, and quality management
- Integrating with Adobe Audition or Premiere
Implementing RNNoise in Custom Systems
- Exploring the RNNoise open-source library
- Compiling and utilizing RNNoise with FFmpeg
- 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, recorded media, and field audio
- Balancing human perception with objective scoring tools
Case Studies and Workflow Implementation
- Setting up enterprise conferencing for legal and financial sectors
- Applying noise reduction in media production pipelines
- Refining audio for evidentiary review and surveillance analysis
Recap and Future Directions
Requirements
- Foundational knowledge of digital audio principles.
- Experience with audio editing or communication software.
Target Audience
- Audio engineers.
- IT support teams.
- Media production professionals.
