[P] Deezer showed CNN detection fails on compressed audio, here's a dual-engine approach that survives MP3

I've been working on detecting AI-generated music and ran into the same wall that Deezer's team documented in their paper, CNN-based detection on mel-spectrograms breaks when audio is compressed to MP3.

The problem: A ResNet18 trained on mel-spectrograms works well on WAV files, but real-world music is distributed as MP3/AAC. Compression destroys the subtle spectral artifacts the CNN relies on.

What actually worked: Instead of trying to make the CNN more robust, I added a second engine based on source separation (Demucs). The idea is simple:

1. Separate a track into 4 stems (vocals, drums, bass, other)
2. Re-mix them back together
3. Measure the difference between original and reconstructed audio

For human-recorded music, stems bleed into each other during recording (room acoustics, mic crosstalk, etc.), so separation + reconstruction produces noticeable differences. For AI music, each stem is synthesized independently separation and reconstruction yield nearly identical results.

Results:

• Human false positive rate: ~1.1%
• AI detection rate: 80%+
• Works regardless of audio codec (MP3, AAC, OGG)

The CNN handles the easy cases (high-confidence predictions), and the reconstruction engine only kicks in when CNN is uncertain. This saves compute since source separation is expensive.

Limitations:

• Detection rate varies across different AI generators
• Demucs is non-deterministic borderline cases can flip between runs
• Only tested on music, not speech or sound effects

Curious if anyone has explored similar hybrid approaches, or has ideas for making the reconstruction analysis more robust.