AI-Powered Audio Enhancement and Noise Reduction

• An understanding of basic digital audio concepts
• Familiarity with using audio editing or communication tools

Audience

• Audio engineers
• IT support teams
• Media production units

AI-Powered Audio Enhancement and Noise Reduction is a practical course designed to introduce participants to modern AI tools used to clean and improve audio in real time or post-production settings.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level professionals who wish to apply AI tools to remove background noise, enhance voice clarity, and improve audio quality across conferencing, broadcast, and surveillance use cases.

By the end of this training, participants will be able to:

• Understand the fundamentals of audio signal processing and common sources of noise.
• Use AI-based tools like Krisp, Adobe Enhance, and RNNoise for real-world audio enhancement.
• Integrate noise reduction into conferencing, recording, or live broadcast workflows.
• Evaluate and choose appropriate tools and models based on quality, latency, and deployment needs.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Fundamentals of Audio and Noise

• Key concepts: waveform, frequency, amplitude, and dynamic range
• Types of noise: environmental, equipment, digital artifacts
• Traditional vs 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 vs 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