arXiv — cs.AI preprintsInternational9 October 2026
How Much Audio Is Left In An Embedding? An Inversion Audit Of Audio Encoders
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arXiv:2610.12250v1 Announce Type: cross Abstract: Pretrained audio encoders are reused for downstream tasks that are often unknown when the encoder is trained, so their usefulness depends partly on which signal properties survive the pretext objective. We study this retained information through paired source reconstruction. Using a shared Stable Audio Open latent diffusion decoder, we reconstruct five-second, 44.1-kHz stereo music from frozen representations produced by supervised classifiers (VGGish, ConvNeXt), an audio-text contrastive model (CLAP), and a waveform-reconstruction model (EnCod
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