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Erschienen in:
2011 | OriginalPaper | Buchkapitel
Improving Tonality Measures for Audio Watermarking
verfasst von : Michael Arnold, Xiao-Ming Chen, Peter G. Baum, Gwenaël Doërr
Erschienen in: Information Hiding
Verlag: Springer Berlin Heidelberg
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Psychoacoustic models are routinely used in audio watermarking algorithms to adjust the changes induced by the watermarking process to the sensitivity of the ear. The performances of such models in audio watermarking applications are tightly related to the determination of tonal and noise-like components. In this paper, we present an improved tonality estimation and its integration into a psychoacoustic model. Instead of conventional binary classification, we exploit bi-modal prediction for more precise tonality estimation. Experimental results show improved robustness of the considered audio watermarking algorithm integrating the new tonality estimation, while preserving the high quality of the audio track.