Dr. Jessada Karnjana, PhD student,Human Life Design Area, received Best Paper Award 2017 in IEICE.
Dr. Jessada Karnjana, PhD student (SIIT-JAIST Doctoral Dual Degree Program) in Unoki Lab. of Human Life Design Area, received Best Paper Award 2017 in IEICE.
Singular-Spectrum Analysis for Digital Audio Watermarking with Automatic Parameterization and Parameter Estimation
Jessada KARNJANA，Masashi UNOKI，Pakinee AIMMANEE，Chai WUTIWIWATCHAI
IEICE Trans. INF. & SYST., Vol. E99-D, No. 8, pp. 2109-2120, Aug. 2016.
This paper proposes a blind, inaudible, robust digital audio watermarking scheme based on singular-spectrum analysis, which relates to watermarking techniques based on singular value decomposition. We decompose a host signal into its oscillatory components and modify amplitudes of some of those components with respect to a watermark bit and embedding rule. To improve the sound quality of a watermarked signal and still maintain robustness, differential evolution is introduced to find optimal parameters of the proposed scheme. Test results show that, although a trade-off between inaudibility and robustness still persists, the difference in sound quality between the original and the watermarked one is considerably smaller. This improved scheme is robust against many attacks, such as MP3 and MP4 compression, and band-pass filtering. However, there is a drawback, i.e., some music-dependent parameters need to be shared between embedding and extraction processes. To overcome this drawback, we propose a method for automatic parameter estimation. By incorporating the estimation method into the framework, those parameters need not to be shared, and the test results show that it can blindly decode watermark bits with an accuracy of 99.99%. This paper not only proposes a new technique and scheme but also discusses the singular value and its physical interpretation.
I am greatly honored to receive this award and to be recognized by committee members and reviewers for our work on Singular-Spectrum Analysis for Digital Audio Watermarking with Automatic Parameterization and Parameter Estimation. I would be lying if I did not say that I was shocked when I first read the award-winning notification letter. This award means a lot to us because it recognizes our effort and contribution to the field of acoustic information science. Numerous people helped bring this work to fruition. My deepest gratitude goes first and foremost to Unoki-sensei, my supervisor at JAIST, for his guidance and support. Every once in a while his analogy between doing research and investigating a crime scene is brought to my mind. We researchers are like detectives who collect, analyze, and use data to serve our purpose; this is more or less what he said. I appreciate his broad and profound knowledge every time we discuss. My heartfelt gratitude is extended to my vice supervisor, Akagi-sensei, whose critical comments and insightful suggestions helped me to improve my knowledge and skills. My sincere thanks also go to two Thai professors and co-advisors, Dr. Pakinee and Dr. Chai, my friends, and AIS-lab members for giving me their time and support. Last but not least, I would like to give many thanks to my parents and sisters who always be by my side. This award is then dedicated to all of them.