A Proactive and Dual Prevention Mechanism against Illegal Song Covers
  empowered by Singing Voice Conversion
Guangke Chen Yedi Zhang Fu Song Ting Wang Xiaoning Du Yang Liu
Singing voice conversion (SVC) automates song covers by converting one
singer's singing voice into another target singer's singing voice with the
original lyrics and melody. However, it raises serious concerns about copyright
and civil right infringements to multiple entities. This work proposes
SongBsAb, the first proactive approach to mitigate unauthorized SVC-based
illegal song covers. SongBsAb introduces human-imperceptible perturbations to
singing voices before releasing them, so that when they are used, the
generation process of SVC will be interfered, resulting in unexpected singing
voices. SongBsAb features a dual prevention effect by causing both (singer)
identity disruption and lyric disruption, namely, the SVC-covered singing voice
neither imitates the target singer nor preserves the original lyrics. To
improve the imperceptibility of perturbations, we refine a psychoacoustic
model-based loss with the backing track as an additional masker, a unique
accompanying element for singing voices compared to ordinary speech voices. To
enhance the transferability, we propose to utilize a frame-level interaction
reduction-based loss. We demonstrate the prevention effectiveness, utility, and
robustness of SongBsAb on three SVC models and two datasets using both
objective and human study-based subjective metrics. Our work fosters an
emerging research direction for mitigating illegal automated song covers.