Neural audio enhancement is the use of deep learning models to improve the perceptual quality of recorded audio by removing noise, reverberation, and artefacts or by restoring lost detail. Models are trained on paired clean and degraded audio to learn a mapping that suppresses unwanted components while preserving the target signal. It is widely applied to speech in podcasting, conferencing, and media post-production.
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- Common approaches operate in the time domain or on spectrograms, using masking, regression, or generative restoration to reconstruct clean audio. Trade-offs centre on suppressing noise aggressively without introducing musical artefacts or distorting the speaker’s voice, and real-time variants must keep latency low enough for live use.