We present a theoretical analysis of the Philips audio fingerprinting method under Gaussian white noise distortion for correlated stationary Gaussian sources. Prior analyses were for white Gaussian sources, which do not model realistically real audio signals. Our approach relies on formulating the unquantized fingerprint as a quadratic form, which affords a systematic way to compute the model parameters. We provide closed-form analytical upper bounds for the probability of bit error of the hash, and we apply these expressions to real audio signals.

We present a 2-D extension of a timing error detector (TED) originally developed by Mueller and Muller for timing recovery on multilevel pulse amplitude modulation (PAM) over 1-D channels. We provide accurate theoretical expressions of the performance of the proposed scheme, and we verify its applicability by studying a particular case. The 2-D TED thus obtained is used as the engine of a 2-D phase locked loop (PLL) for timing recovery.

We present a novel theoretical analysis of the Philips audio fingerprinting method proposed by Haitsma, Kalker, and Oostveen (2001). Although this robust hashing algorithm exhibits very good performance, the method has only been partially analyzed in the literature. Hence, there is a clear need for a more complete analysis which allows both performance prediction and systematic optimization. We examine here the theoretical performance of the method for Gaussian inputs by means of a statistical model. Our analysis relies on formulating the unquantized fingerprint as a quadratic form, which affords a systematic way to compute the model parameters. We provide closed-form analytical upperbounds for the probability of bit error of the hash for two relevant scenarios: noise addition and desynchronization. We show that these results are useful when applied to real audio signals

A new approach to synchronization recovery for signals watermarked using the dither modulation data hiding scheme is presented. The strategy followed involves the use of a digital phase-locked loop to track the offsets applied by an attacker to the sampling grid of the watermarked signal. The main element in this synchronization loop is the timing error detector which is responsible for generating an error signal, used to update the estimates of the applied offsets. It is shown how a timing error detector which has been used in digital communications may be easily adapted to extract timing information from DM watermarked signals. The performance of the proposed synchronizer is evaluated using the probability of decoding error under different models for the sampling grid offsets

In this paper, we propose a framework that enables the detection of noise in recommender system databases. We consider two classes of noise: natural and malicious noise. The issue of natural noise arises from imperfect user behaviour (e.g. erroneous/careless preference selection) and the various rating collection processes that are employed. Malicious noise concerns the deliberate attempt to bias system output in some particular manner. We argue that both classes of noise are important and can adversely effect recommendation performance. Our objective is to devise techniques that enable system administrators to identify and remove from the recommendation process any such noise that is present in the data. We provide an empirical evaluation of our approach and demonstrate that it is successful with respect to key performance indicators.

This paper presents software tools to simulate DSP algorithms on a wide variety of fixed point architectures including microprocessors, DSPs and FPGA devices. Existing solutions for evaluating the signal quality in fixed point algorithms are either unable to deal with non-linear systems, fail to consider the architectural details of the target device or do not produce a real output that can be used in subjective testing. Using example non-linear algorithms it is shown that architectural details must be considered when evaluating numerical performance.

The vulnerability of quantization-based data hiding methods to amplitude scaling requires the formulation of countermeasures to this relatively simple attack. Rational dither modulation (RDM) was recently proposed as a quantization-based data hiding scheme, which is scaling invariant. This is achieved by a simple modification of the well-known dither modulation (DM) data hiding method. The performance of RDM, asymptotically in the memory size of the system, can be made to approach that of DM on the Gaussian channel. In this work we examine the application of near-optimal channel coding to RDM. The channel model required to undertake iterative decoding is presented and the proposed method is applied to real image data.

Soft hashing, also known as robust hashing or perceptual hashing, consists of summarising multimedia data, so as to obtain a concise representation called a hash value. There has been an increasing interest in the soft hashing problem recently. Techniques implementing soft hashing intend to mirror the behaviour of cryptographic hashing, when the information to be hashed can be subject to different kinds of distortion. Many heuristic techniques for undertaking soft hashing of images and other multimedia data have been devised. Except for some attempts, a framework giving solid guidelines to solve the problem is largely lacking. We provide one possible approach to undertake the modelling of robust soft hashing, detailing the basic problems involved. We show how some prior schemes partly fit into our model.

In this paper we propose novel neighbourhood formation and similarity weight transformation schemes for automated collaborative filtering systems. We demonstrate the benefits of our schemes from the point-of-view of the efficiency and robustness provided, while achieving the accuracy and coverage of a benchmark k-Nearest Neighbour (k-NN) model.

Collaborative recommendation has emerged as an effective technique for personalized information access. However, there has been relatively little theoretical analysis of the conditions under which the technique is effective. To explore this issue, we analyse the robustness of collaborative recommendation: the ability to make recommendations despite (possibly intentional) noisy product ratings. There are two aspects to robustness: recommendation accuracy and stability. We formalize recommendation accuracy in machine learning terms and develop theoretically justified models of accuracy. In addition, we present a framework to examine recommendation stability in the context of a widely-used collaborative filtering algorithm. For each case, we evaluate our analysis using several real-world data-sets. Our investigation is both practically relevant for enterprises wondering whether collaborative recommendation leaves their marketing operations open to attack, and theoretically interesting for the light it sheds on a comprehensive theory of collaborative recommendation.

Spread spectrum watermarking proceeds by extracting a feature vector from the cover contents and embedding a pseudo-random watermark signal in that feature vector. To detect the presence of the watermark, a correlation of the feature vector with the pseudorandom signal is performed and the result compared to a threshold. This correlation detection function is a first-order function of the feature vector components. In recent work, we have proposed that higher-order polynomial detection functions, combined with a side-informed watermark embedding strategy, can be used to increase the efficiency of the watermarking system. We have demonstrated this through a statistical analysis. In this paper, we apply our new family of detection functions to the watermarking of real audio signals. The schemes are tested on a database of over 300 different audio signals and a robustness analysis is performed on the experimental results.

Spread spectrum watermarking proceeds by extracting a feature vector from the cover contents and embedding a pseudo-random watermark signal in that feature vector. To detect the presence of the watermark, a correlation of the feature vector with the pseudo-random signal is performed and the result compared to a threshold. This correlation detection function is a first-order function of the feature vector components. In recent work, we have proposed that higher-order polynomial detection functions, combined with a side-informed watermark embedding strategy, can be used to increase the efficiency of the watermarking system. We have demonstrated this through a statistical analysis. In this paper, we apply our new family of detection functions to the watermarking of real audio signals. The schemes are tested on a database of over 300 different audio signals and a robustness analysis is performed on the experimental results.

Abstract Summary: Digital Extractor is a program for the high-throughput processing of data sets derived from digital differential display-based comparisons of EST libraries. These comparisons can be utilized to identify discrete subsets of genes whose expression is restricted to distinct tissue types. The program facilitates these investigations by permitting parallel annotation of genes identified as being differentially expressed. Availability: The executable program, suitable for use on all UNIX-based platforms is freely available to non-profit users. Contact: [email protected] * To whom correspondence should be addressed.

This paper deals with some detection issues of watermark signals. We propose an easy way to implement an informed watermarking embedder whatever the detection function. This method shows that a linear detection function is not suitable for side information. This is the reason why we build a family of nonlinear functions named JANIS. Used with a side-informed embedder, its performance is much better than the classical spread spectrum method.

An informed audio watermarking scheme using chaotic waveforms is proposed. The watermark consists of an attenuated signal embedded in the original audio signal. A modulation scheme based on vector quantization is used to achieve blind detection of the watermark signal. A method for self-synchronization of the watermark detector is proposed using chaotic orthogonal waveforms. A perceptual model is employed to ensure inaudibility of the embedded signal that is shaped to the spectrum of the host signal. High robustness to external noise is achieved by spreading the watermark data over a period of time. The robustness of the scheme is assessed by computer simulations.

A watermarking technique is proposed for embedding a significant amount of data in digital still images while retaining a high perceptual quality. The scheme employs digital communication techniques to achieve high robustness to standard image processing operations. Information is embedded in the frequency domain by modulating selected DFT values of the image and using sets of orthogonal codes in a fashion similar to CDMA. DFT values are grouped into different bands defining independent channels for carrying data. Blind recovery of the embedded data is achieved by analyzing the DFT values of the watermarked image. The performance of the proposed adaptive scheme is evaluated by computer simulations.