English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents a proposed robust speech watermarking approach. This approach aims to increase the speech watermarking robustness against different attacks. The method is based on Empirical Mode Decomposition (EMD) and Singular Value Decomposition (SVD). The speech signal is decomposed by EMD into its Intrinsic Mode Functions (IMFs), the first IMF transform to a 2-D format. The watermark image embedded into the singular values (SVs) of the first IMF. After watermark embedding, the speech signal transformed back into a1-D format. The first IMF preserves the speech signal perceptual quality, which leads to preserving the watermarked signal imperceptibility. The singular values matrix is stable against any small perturbation happens to the original signal which provide more secure and robustness against attacks. The proposed approach can be implemented on the speech signal as a whole or as a blocks. Block-based SVD implementation allows embedding more than one watermark in the speech signal which increase the opportunities and efficiency of watermark extraction in the presence of attacks. Simulation results show that using of EMD with SVD enhance the watermark extraction especially in the presence of attacks. A block-based implementation of the proposed speech watermarking also realize a higher correlation coefficient in the presence of attacks.

Speech Watermarking using a Hybrid Strategy of both Empirical Mode Decomposition and Singular Value Decomposition