AI in Sound Engineering
A sober look at AI-assisted audio tools: what they do well today, what they do badly, and how to stay responsible for the result.
This module exists because these tools are already in professional use, and pretending otherwise would leave a gap in the training. It is taught at the level of workflow and judgement rather than as a product tutorial: software changes faster than a curriculum can, and the underlying questions do not.
The emphasis throughout is on evaluation. An AI-assisted process can rescue material that was previously unusable, and it can also introduce artefacts that a client will notice on a better system than yours. Learning to hear the difference is the skill worth having.
Responsibility is part of the module rather than an appendix. If a process changes a performance, removes a sound or reconstructs something that was never recorded, the engineer is still answerable for what was delivered.
Curriculum
Where these tools sit
- Which stages of a workflow AI assistance currently touches
- What kind of task these systems are suited to
- How to evaluate a tool instead of trusting a demo
- Cost, licensing and dependency considerations
Restoration and repair
- Noise, hum and click reduction
- Reverb reduction and its artefacts
- Recovering unusable material — and recognising when you cannot
- Judging processing by listening, not by the meter
Transcription and logging
- Speech-to-text for dialogue logging
- Marking and searching long recordings
- Accuracy limits with accents, overlap and noise
- Where a human pass is still required
Source separation
- Splitting a mix into approximate parts
- Realistic uses — reference work, repair, teaching
- Recognisable separation artefacts
- Why a separated stem is not a multitrack
Editing and mixing assistance
- Assisted editing and take selection
- Automatic level and tonal matching
- Using suggestions as a starting point
- Keeping your own decision in the chain
Responsible use
- Consent, rights and voice-related questions
- Disclosure and client expectations
- Data handling when material leaves your machine
- Professional accountability for the delivered result
What you will be able to do
- Judge whether an AI-assisted tool is appropriate for a given task.
- Recognise the characteristic artefacts these processes leave behind.
- Use restoration and separation tools without overstating what they achieved.
- Explain to a client what was processed and how.
Practical components
Exercises compare a processed result against the original by listening, so the trade-off is heard rather than assumed. Which specific tools are used depends on the licences available at the time.