  In the realm of audio watermarking, it is challenging to simultaneously
encode imperceptible messages while enhancing the message capacity and
robustness. Although recent advancements in deep learning-based methods bolster
the message capacity and robustness over traditional methods, the encoded
messages introduce audible artefacts that restricts their usage in professional
settings. In this study, we introduce three key innovations. Firstly, our work
is the first deep learning-based model to integrate psychoacoustic model based
thresholding to achieve imperceptible watermarks. Secondly, we introduce
psuedo-differentiable compression layers, enhancing the robustness of our
watermarking algorithm. Lastly, we introduce a method to eliminate the need for
perceptual losses, enabling us to achieve SOTA in both robustness as well as
imperceptible watermarking. Our contributions lead us to SilentCipher, a model
enabling users to encode messages within audio signals sampled at 44.1kHz.
