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Course Outline
Fundamentals of Audio and Noise
- Key concepts: waveform, frequency, amplitude, and dynamic range.
- Types of noise: environmental, equipment, and digital artifacts.
- Traditional versus AI-driven noise reduction approaches.
Overview of AI-Based Audio Enhancement Tools
- How AI models process and clean audio.
- Tool comparison: Krisp, Adobe Enhance, RNNoise, NVIDIA RTX Voice.
- Deployment options: local, cloud, and real-time integration.
Using Krisp for Real-Time Conferencing
- Installation and setup on Windows/macOS.
- Integration with Zoom, Teams, and Skype.
- Live audio tests and troubleshooting common issues.
Enhancing Recordings with Adobe Enhance
- Uploading and cleaning podcast-style recordings.
- Limitations, latency, and quality control.
- Using in combination with Adobe Audition or Premiere.
Deploying RNNoise in Custom Pipelines
- Overview of RNNoise open-source library.
- Compiling and using RNNoise with FFmpeg.
- Custom integrations in surveillance or VoIP systems.
Evaluating Quality and Performance
- Metrics: signal-to-noise ratio, latency, CPU/GPU impact.
- Testing across use cases: meetings, recordings, field audio.
- Human perception versus objective scoring tools.
Case Studies and Workflow Integration
- Enterprise conferencing setup for legal and finance sectors.
- Noise reduction in media production pipelines.
- Audio cleaning for evidence and surveillance review.
Summary and Next Steps
Requirements
- Understanding of basic digital audio concepts.
- Familiarity with using audio editing or communication tools.
Audience
- Audio engineers.
- IT support teams.
- Media production units.
14 Hours
