Speech Watermarking Based on Robust Principal Component Analysis and Formant Manipulations

This paper proposes a watermarking method for speech signals based on Robust Principal Component Analysis (RPCA) and formant manipulations. As the spectrogram of speech has a relatively sparse structure, the core information of speech is extracted into a sparse matrix using RPCA so that formants can...

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Bibliographic Details
Published in2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) pp. 2082 - 2086
Main Authors Wang, Shengbei, Yuan, Weitao, Wang, Jianming, Unoki, Masashi
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2018
Subjects
Estimation
Formant
Line spectral frequencies
Linear prediction
Noise measurement
Principal component analysis
Robust principal component analysis
Robustness
Sparse matrices
Speech codecs
Watermarking
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Summary:This paper proposes a watermarking method for speech signals based on Robust Principal Component Analysis (RPCA) and formant manipulations. As the spectrogram of speech has a relatively sparse structure, the core information of speech is extracted into a sparse matrix using RPCA so that formants can be estimated with Linear Prediction (LP) more accurately even under noise/interferences, which significantly improves the robustness of proposed method. We investigate how the formants can be controlled and manipulated to make the watermarking method effective. Watermarks are embedded into speech by controlling the shape and power of formants using the stable and robust parameter, i.e., line spectral frequencies (LSFs). Evaluations regarding inaudibility and robustness are carried out and the results suggest that the proposed method can not only satisfy inaudibility but also provide good robustness against general processing and different speech codecs which is better than the other methods.
ISSN:2379-190X
DOI:10.1109/ICASSP.2018.8462356