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Regenerating audio, whether by running it through a neural codec or vocoder or by re-recording it in a room, is the only operation that produces a natively consistent file, but it does not make the audio untraceable; it swaps one fingerprint for another. If your goal is a file with no internal seams, regeneration is the tool. If your goal is genuine anonymity, it falls short, and the reason is worth understanding before you rely on it.
Why scrubbing trace by trace hits a wall
Removing individual traces from a recording, the metadata, then the hum, then the noise floor, runs into a consistency problem. The signals that remain have to agree with one another, and an examiner cross-checks them. Scrub one and you can leave a seam where it used to be. Beyond a certain point the file is not cleaner, it is a patchwork, and the patchwork itself is evidence. This is why the idea of a full reset is appealing: instead of editing the surviving traces one by one, you replace the whole signal with a fresh one that has no history. That is the appeal, and it is real. It is also where most people stop thinking, because a file with no seams feels like a file with no story, and those are not the same thing.
What regeneration is, and why it appears to work
Regeneration means re-encoding the audio through a neural codec or vocoder, or physically playing it and recording it again. Müller and colleagues studied the re-recording version against synthetic-speech detectors and found it genuinely defeats them: a top detector’s equal error rate surged from 4.7 percent to 18.2 percent once samples were played and re-recorded. The mechanism is erasure of the fine detail a model keys on, “removing subtle artifacts that detection models rely on for identification.” The regenerated file is internally consistent because it was, in a real sense, freshly made. There are no seams because there was no editing.
It is worth being concrete about that jump. An equal error rate near 4.7 percent is a detector that is almost always right; at 18.2 percent it is wrong close to one time in five, which is the difference between a usable forensic tool and one a court would discount. Re-recording moved the detector across that line.
The word reset is doing a lot of work there, though. It does not mean the trace disappears. It means the old trace is replaced by a new one.
The catch: it stamps a new signature
A regenerated file is not a blank file. It carries the fingerprint of whatever regenerated it. Moussa and colleagues showed that passing speech through a neural codec leaves a codec-identifying trace: “distinctive frequency artefacts enable for identifying neurally compressed audio and fingerprint specific AI-based codecs.” Run your audio through EnCodec, Lyra or a similar system to reset it, and you have stamped that codec’s signature onto the output.
Re-recording does the same thing physically. The Müller work notes that replay captures “playback distortions introduced by real-world acoustic environments and hardware characteristics,” meaning the loudspeaker, the room and the microphone you used. That trace is partly recoverable: once detectors were retrained on room-impulse-response data, the error rate came back down to 11.0 percent. The new fingerprint is fresher, but it is still a fingerprint, and the examiner can adapt to it.
What regeneration does not reset
If any part of the chain still contains a real microphone capture, that device fingerprint survives. Qamhan and colleagues identified source microphones from the signal at between 97.6 percent and 99.98 percent accuracy, and regeneration through a codec does not erase a capture that happened upstream of it. Chuang, Garg and Wu framed the whole contest as “the dynamic interplay between forensic analysts and adversaries,” and their finding holds here: every concealment step has a cost, and the analyst gets to respond. Reset the chain and you have handed the examiner a new problem, not no problem.
There is a plainer cost too. Regeneration rebuilds the waveform, and re-recording sends it through the air, so both trade audio fidelity for the reset. A version clean enough to defeat a detector may no longer sound like the original, which for anything meant as a faithful record is its own kind of failure.
Differently traceable, not untraceable
Regeneration is the real tool for producing a consistent file, but consistency is not anonymity. You have not removed the audio’s traceability; you have changed which fingerprint it carries and reset the clock on the examiner. The tag was the label; the codec artefact and the room are the new evidence. If your specific question is whether re-recording strips a watermark, see does re-recording or re-encoding remove a watermark. If it is whether the result still reads as AI-generated, see is this song AI-generated?.
Sources
- Chuang, Garg, Wu (2013). Anti-Forensics and Countermeasures of Electrical Network Frequency Analysis. IEEE Transactions on Information Forensics and Security.
- Moussa, Bergmann, Riess (2024). Unmasking Neural Codecs: Forensic Identification of AI-compressed Speech. Interspeech.
- Müller, Kawa, Choong, Stan, Bukkapatnam, Pizzi, Wagner, Sperl (2025). Replay Attacks Against Audio Deepfake Detection.
- Qamhan, Alotaibi, Selouani (2023). Source Microphone Identification Using Swin Transformer. Applied Sciences.