Jessada Karnjana

An Audio Watermarking Scheme Based on Singular-Spectrum Analysis

This paper proposes a blind audio watermarking scheme based on singular-spectrum analysis (SSA) which relates to several techniques based on singular value decomposition (SVD). SSA is used to decompose a signal into several additive oscillatory components where each component represents a simple oscillatory mode. The proposed scheme embedded a watermark into a host signal by modifying scaling factors of certain components of the signal. Test results show that the proposed scheme satisfies imperceptibility criterion suggested by IHC with the average ODG of 0.18. It is robust against many attacks, such as MP3 and MP4 compression, band-pass filtering, and re-sampling. This paper does not only propose a new watermarking scheme, it also discusses the singular value and reveals its meaning, which has been deployed and played an important role in all SVD-based schemes.

An audio watermarking scheme based on automatic parameterized singular-spectrum analysis using differential evolution

This paper proposes an audio watermarking scheme based on singular-spectrum analysis (SSA) and differential evolution. In our framework, a watermark is embedded into an audio signal by modifying the amplitude of some oscillatory components which are decomposed by SSA, and a parameter set for the modification is determined by differential evolution. Test results showed that, although there is a trade-off between inaudibility and robustness, the sound quality of watermarked signal could be improved considerably while the bit error rate could be satisfied. Our proposed scheme is inaudible and robust. Furthermore, based on analyzing the second derivative of singular spectrum, it was found that our proposed scheme can be completely blind.

Improving Accuracy of Dissolved Oxygen Measurement in an Automatic Aerator-Control System for Shrimp Farming by Kalman Filtering

In automatic aerator-control systems used for shrimp farming, the dissolved oxygen (DO) measurement is one of the crucial parts since it affects both the quantity and quality of the product yield. It goes without saying that the more accurate the DO sensor, the more expensive it is. In this paper, we propose a technique for accuracy improvement of the DO measurement of a low-cost sensor by applying the Kalman filtering with an autoregressive model (AR). This work aims to minimize the difference between DO values read from the accurate sensor and those from less accurate sensors. Based on the standard Kalman filtering algorithm, data obtained from one low-cost sensor together with an AR of order 1 are used in the prediction stage, and data obtained from another sensor are used in the measurement update stage. Experimental results show that this technique can improve the measurement accuracy between approximately \(10\%\) and \(19\%\).

Tampering Detection in Speech Signals by Semi-Fragile Watermarking Based on Singular-Spectrum Analysis

To solve the problem of unauthorized modification in speech signals, this paper proposes a novel speech-tampering-detection scheme by using the semi-fragile watermarking based on the singular-spectrum analysis (SSA). The SSA is used to analyze the speech signals of which the singular spectra are extracted. The watermark (e.g., signature in-formation) is embedded into those signals by modifying some parts of the singular spectra according to the watermark bit. By comparing the extracted watermark with the original one, the tampered segments of the speech signals are identified and located. The evaluation results show that the proposed scheme is fragile to several malicious attacks but robust against other signal-processing operations. It also satisfies the inaudibility criteria. The proposed scheme not only can locate the tampered locations, but it also can make a prediction about the tampering types and the tampering strength.

SSA-based audio-information-hiding scheme with psychoacoustic model

This paper proposes an inaudible and robust audio-information-hiding scheme based on the singular-spectrum analysis (SSA) and a psychoacoustic model. SSA is used to decompose the host signals into several additive oscillatory components. The hidden information is embedded into the host signals by modifying amplitudes of some oscillatory components. To satisfy the inaudibility, we propose a novel method which is based on the psychoacoustic model to choose the oscillatory components that distort the host signal only slightly after the modification. Accordingly, to associate the output from the psychoacoustic model with the SSA-based scheme, the relationship between the frequency components and singular-value indices is established. The test results show that the proposed scheme achieves both inaudibility and robustness. In addition, compared with the previously proposed scheme based on automatic parameterized SSA using the differential evolution, this proposed scheme has more advantage in terms of reducing the computational time.

Audio Watermarking Scheme Based on Singular Spectrum Analysis and Psychoacoustic Model with Self-Synchronization

This paper proposes a blind, inaudible, and robust audio watermarking scheme based on singular spectrum analysis SSA and the psychoacoustic model 1 ISO/IEC 11172-3. In this work, SSA is used to analyze the host signals and to extract the singular spectra. A watermark is embedded into the host signals by modifying the singular spectra which are in the convex part of the singular spectrum curve so that this part becomes concave. This modification certainly affects the inaudibility and robustness properties of the watermarking scheme. To satisfy both properties, the modified part of the singular spectrum is determined by a novel parameter selection method based on the psychoacoustic model. The test results showed that the proposed scheme achieves not only inaudibility and robustness but also blindness. In addition, this work showed that the extraction process of a variant of the proposed scheme can extract the watermark without assuming to know the frame positions in advance and without embedding additional synchronization code into the audio content.