L.M. Patnaik

Work place: Department of Electronics Systems Engineering, Indian Institute of Science, Bangalore, INDIA

E-mail: lalit@micro.iisc.ernet.in


Research Interests: Type Systems, Information Systems, Systems Architecture, Computer systems and computational processes


Prof. L. M. Patnaik obtained his Ph.D in 1978 in the area of Real-Time Systems, D.Sc. in 1989 in the areas of Computer Systems and Architectures, both from the Indian Institute of Science, Bangalore. During March 2008 – August 2011, he was the Vice Chancellor, Defence Institute of Advanced Technology, Deemed University, Pune. Currently he is an Honorary Professor with the Centre for Electronic Design Technology, Indian Institute of Science, Bangalore.

Author Articles
Adaptive Quantization Index Modulation Audio Watermarking based on Fuzzy Inference System

By Sunita V. Dhavale Rajendra S. Deodhar Debasish Pradhan L.M. Patnaik

DOI: https://doi.org/10.5815/ijigsp.2014.03.01, Pub. Date: 8 Feb. 2014

Many of the adaptive watermarking schemes reported in the literature consider only local audio signal properties. Many schemes require complex computation along with manual parameter settings. In this paper, we propose a novel, fuzzy, adaptive audio watermarking algorithm based on both global and local audio signal properties. The algorithm performs well for dynamic range of audio signals without requiring manual initial parameter selection. Here, mean value of energy (MVE) and variance of spectral flux (VSF) of a given audio signal constitutes global components, while the energy of each audio frame acts as local component. The Quantization Index Modulation (QIM) step size Δ is made adaptive to both the global and local features. The global component automates the initial selection of Δ using the fuzzy inference system while the local component controls the variation in it based on the energy of individual audio frame. Hence Δ adaptively controls the strength of watermark to meet both the robustness and inaudibility requirements, making the system independent of audio nature. Experimental results reveal that our adaptive scheme outperforms other fixed step sized QIM schemes and adaptive schemes and is highly robust against general attacks.

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