Noise-to-mask Ratio Loss for Deep Neural Network based Audio Watermarking

Autor:	Moritz, Martin, Olán, Toni, Virtanen, Tuomas
Rok vydání:	2024
Předmět:	Electrical Engineering and Systems Science - Audio and Speech Processing Computer Science - Sound
Druh dokumentu:	Working Paper
DOI:	10.1109/IS262782.2024.10704132
Popis:	Digital audio watermarking consists in inserting a message into audio signals in a transparent way and can be used to allow automatic recognition of audio material and management of the copyrights. We propose a perceptual loss function to be used in deep neural network based audio watermarking systems. The loss is based on the noise-to-mask ratio (NMR), which is a model of the psychoacoustic masking effect characteristic of the human ear. We use the NMR loss between marked and host signals to train the deep neural models and we evaluate the objective quality with PEAQ and the subjective quality with a MUSHRA test. Both objective and subjective tests show that models trained with NMR loss generate more transparent watermarks than models trained with the conventionally used MSE loss Comment: 6 pages, 7 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.15553 Zobrazit plný text záznamu View this record from Arxiv