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Signal Processing Letters, IEEE

Issue 2 • Date Feb. 2011

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Displaying Results 1 - 25 of 26
  • [Front cover]

    Page(s): C1
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  • IEEE Signal Processing Letters publication information

    Page(s): C2
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  • Table of contents

    Page(s): 81 - 82
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  • Beta-Divergence as a Subclass of Bregman Divergence

    Page(s): 83 - 86
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (106 KB) |  | HTML iconHTML  

    In this paper, we present a complete proof that the β-divergence is a particular case of Bregman divergence. This little-known result makes it possible to straightforwardly apply theorems about Bregman divergences to β-divergences. This is of interest for numerous applications since these divergences are widely used, for instance in non-negative matrix factorization (NMF). View full abstract»

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  • A Novel Low-Complexity HMM Similarity Measure

    Page(s): 87 - 90
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (268 KB) |  | HTML iconHTML  

    In this letter, we propose a novel similarity measure for comparing Hidden Markov models (HMMs) and an efficient scheme for its computation. In the proposed approach, we probabilistically evaluate the correspondence, or goodness of match, between every pair of states in the respective HMMs, based on the concept of semi-Markov random walk. We show that this correspondence score reflects the contribution of a given state pair to the overall similarity between the two HMMs. For similar HMMs, each state in one HMM is expected to have only a few matching states in the other HMM, resulting in a sparse state correspondence score matrix. This allows us to measure the similarity between HMMs by evaluating the sparsity of the state correspondence matrix. Estimation of the proposed similarity score does not require time-consuming Monte-Carlo simulations, hence it can be computed much more efficiently compared to the Kullback-Leibler divergence (KLD) thas has been widely used. We demonstrate the effectiveness of the proposed measure through several examples. View full abstract»

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  • Joint Relay Selection and Power Allocation for Two-Way Relay Networks

    Page(s): 91 - 94
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (171 KB) |  | HTML iconHTML  

    In this letter, we present an optimal joint relay selection (RS) and power allocation scheme for two-way relay networks which aim to establish a communication link between two transceivers with the help of one relay. Our approach is based on the maximization of the smaller of the received signal-to-noise-ratios (SNRs) of the two transceivers under a total transmit power budget. We show that this problem has a closed-form solution and requires only a single integer parameter (i.e, the index of the optimally selected relay) to be broadcasted to all relays. We also show that for large values of the total transmit power, the selection criterion can be approximated as the harmonic mean of the amplitudes of the relays' local channel coefficients. We evaluate the performance of our scheme numerically. View full abstract»

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  • A New Histogram Modification Based Reversible Data Hiding Algorithm Considering the Human Visual System

    Page(s): 95 - 98
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1274 KB) |  | HTML iconHTML  

    In this letter, we propose an improved histogram modification based reversible data hiding technique. In the proposed algorithm, unlike the conventional reversible techniques, a data embedding level is adaptively adjusted for each pixel with a consideration of the human visual system (HVS) characteristics. To this end, an edge and the just noticeable difference (JND) values are estimated for every pixel, and the estimated values are used to determine the embedding level. This pixel level adjustment can effectively reduce the distortion caused by data embedding. The experimental results and performance comparison with other reversible data hiding algorithms are presented to demonstrate the validity of the proposed algorithm. View full abstract»

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  • Factored MLLR Adaptation

    Page(s): 99 - 102
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (159 KB) |  | HTML iconHTML  

    One of the most popular approaches to parameter adaptation in hidden Markov model (HMM) based systems is the maximum likelihood linear regression (MLLR) technique. In this letter, we extend MLLR to factored MLLR (FMLLR) in which the MLLR parameters depend on a continuous-valued control vector. Since it is practically impossible to estimate the MLLR parameters for each control vector separately, we propose a compact parametric form of the MLLR parameters. In the proposed approach, each MLLR parameter is represented as an inner product between a regression vector and transformed control vector. We present an algorithm to train the FMLLR parameters based on a general framework of the expectation-maximization (EM) algorithm. The proposed approach is applied to adapt the HMM parameters obtained from a database of reading-style speech to singing-style voices while treating the pitches and durations extracted from the musical notes as the control vectors. This enables to efficiently construct a singing voice synthesizer with only a small amount of singing data. View full abstract»

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  • Mean Square Error Estimation in Thresholding

    Page(s): 103 - 106
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (225 KB) |  | HTML iconHTML  

    We present a novel approach to estimating the mean square error (MSE) associated with any given threshold level in both hard and soft thresholding. The estimate is provided by using only the data that is being thresholded. This adaptive approach provides probabilistic confidence bounds on the MSE. The MSE bounds can be used to evaluate the denoising method. Our simulation results confirm that not only does the method provide an accurate estimate of the MSE for any given thresholding method, but the proposed method can also search and find an optimum threshold for any noisy data with regard to MSE. View full abstract»

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  • An Iterative Approach to Near-Uniform Group-Delay Error Design of FIR Filters

    Page(s): 107 - 110
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (204 KB) |  | HTML iconHTML  

    For a minimax finite impulse response filter, the group-delay error near the bandedges of the filter is usually much larger than the error elsewhere. A sigmoid function was recently introduced as a phase-error upper-bound function to effectively reduce the group-delay error near the bandedge. However, the manual selection of the function's parameters limits its flexible use in the design. This letter presents an iterative procedure to update the phase-error upper-bound function using a modified envelope of the absolute group-delay error, such that the resulting filter has near-uniform group-delay error, and thus to reduce the maximum group-delay error. Design examples demonstrate the effectiveness of the proposed iterative procedure. View full abstract»

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  • Time-Recursive IAA Spectral Estimation

    Page(s): 111 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (475 KB) |  | HTML iconHTML  

    This letter presents computationally efficient time-updating algorithms of the recent Iterative Adaptive Approach (IAA) spectral estimation technique. By exploiting the inherently low displacement rank, together with the development of suitable Gohberg-Semencul (GS) representations, and the use of data dependent trigonometric polynomials, the proposed time-recursive IAA algorithm offers a reduction of the necessary computational complexity with at least one order of magnitude. The resulting complexity can also be reduced further by allowing for approximate solutions. Numerical simulations together with theoretical complexity measures illustrate the achieved performance gain. View full abstract»

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  • Privacy Protection of Fingerprint Database

    Page(s): 115 - 118
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (419 KB) |  | HTML iconHTML  

    A fingerprint authentication system for the privacy protection of the fingerprint template stored in a database is introduced here. The considered fingerprint data is a binary thinned fingerprint image, which will be embedded with some private user information without causing obvious abnormality in the enrollment phase. In the authentication phase, these hidden user data can be extracted from the stored template for verifying the authenticity of the person who provides the query fingerprint. A novel data hiding scheme is proposed for the thinned fingerprint template. This scheme does not produce any boundary pixel in the thinned fingerprint during data embedding. Thus, the abnormality caused by data hiding is visually imperceptible in the marked-thinned fingerprint. Compared with using existing binary image data hiding techniques, the proposed method causes the least abnormality for a thinned fingerprint without compromising the performance of the fingerprint identification. View full abstract»

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  • High Speed Progressive Digital-Reversal Algorithm

    Page(s): 119 - 121
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (233 KB) |  | HTML iconHTML  

    This paper describes a number of fast digital-reversal algorithms based on progressive index mapping. The proposed digital-reversal approach can obtain higher efficiency by decreasing the number of index mapping operations, unrolling the innermost loop, and vectorizing data operations. Comparing with other digital-reversal algorithms, we find that the new approach have higher efficiency than the known fastest algorithms. View full abstract»

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  • Letter-to-Sound Pronunciation Prediction Using Conditional Random Fields

    Page(s): 122 - 125
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (104 KB) |  | HTML iconHTML  

    Pronunciation prediction, or letter-to-sound (LTS) conversion, is an essential task for speech synthesis, open vocabulary spoken term detection and other applications dealing with novel words. Most current approaches (at least for English) employ data-driven methods to learn and represent pronunciation “rules” using statistical models such as decision trees, hidden Markov models (HMMs) or joint-multigram models (JMMs). The LTS task remains challenging, particularly for languages with a complex relationship between spelling and pronunciation such as English. In this paper, we propose to use a conditional random field (CRF) to perform LTS because it avoids having to model a distribution over observations and can perform global inference, suggesting that it may be more suitable for LTS than decision trees, HMMs or JMMs. One challenge in applying CRFs to LTS is that the phoneme and grapheme sequences of a word are generally of different lengths, which makes CRF training difficult. To solve this problem, we employed a joint-multigram model to generate aligned training exemplars. Experiments conducted with the AMI05 dictionary demonstrate that a CRF significantly outperforms other models, especially if n-best lists of predictions are generated. View full abstract»

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  • Coherent Spectral Analysis of Asynchronously Sampled Signals

    Page(s): 126 - 129
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (556 KB) |  | HTML iconHTML  

    We propose a joint sparse signal recovery approach to coherent spectral analysis of irregularly sampled signals. These signals share the same frequencies, and are sampled asynchronously. Two types of solution procedures are considered. First one is a convex optimization approach, which optimizes a mixed l2,1-norm. The other method minimizes an approximation of l2,0-norm and the resulting algorithm can be implemented using a few FFTs and IFFTs. We demonstrate the effectiveness of the sparse recovery approach using simulation experiments. In particular, the l2,0 approximation approach is very fast. In addition, it offers increased resolution, improved robustness to noise, and works well with limited number of data samples. View full abstract»

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  • Spectrogram Image Feature for Sound Event Classification in Mismatched Conditions

    Page(s): 130 - 133
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (295 KB) |  | HTML iconHTML  

    In this letter, we present a novel feature extraction method for sound event classification, based on the visual signature extracted from the sound's time-frequency representation. The motivation stems from the fact that spectrograms form recognisable images, that can be identified by a human reader, with perception enhanced by pseudo-coloration of the image. The signal processing in our method is as follows. 1) The spectrogram is normalised into greyscale with a fixed range. 2) The dynamic range is quantized into regions, each of which is then mapped to form a monochrome image. 3) The monochrome images are partitioned into blocks, and the distribution statistics in each block are extracted to form the feature. The robustness of the proposed method comes from the fact that the noise is normally more diffuse than the signal and therefore the effect of the noise is limited to a particular quantization region, leaving the other regions less changed. The method is tested on a database of 60 sound classes containing a mixture of collision, action and characteristic sounds and shows a significant improvement over other methods in mismatched conditions, without the need for noise reduction. View full abstract»

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  • Relay Selection in Dual-Hop Vehicular Networks

    Page(s): 134 - 137
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (351 KB) |  | HTML iconHTML  

    In this letter, we investigate cooperative diversity with relay selection over cascaded Rayleigh fading channels. In particular, we analyze the performance of a relay selection scheme for cooperative vehicular networks with the decode-and-forward (DF) protocol. Only the “best” relay, which satisfies an index of merit, is selected. We ignore the direct transmission between the source (S) and its destination (D), and assume that the destination has perfect knowledge of the SR and RD channel gains. We study the performance of the underlying scheme in terms of outage probability and investigate its achievable diversity order. View full abstract»

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  • Logarithmic Frequency Scale Parallel Filter Design With Complex and Magnitude-Only Specifications

    Page(s): 138 - 141
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (163 KB) |  | HTML iconHTML  

    Recently, the fixed-pole design of second-order parallel filters has been introduced to accomplish arbitrary (e.g., logarithmic) frequency resolution for transfer function modeling and equalization. The frequency resolution is set by the pole frequencies, and the resulting filter response corresponds to the smoothed (moving-average filtered) version of the target frequency response. This letter presents the frequency-domain version of the design algorithm for complex and real filter coefficients. The proposed frequency-domain design, besides its computational benefits, allows the use of frequency weighting. In addition, a magnitude-only variation of the algorithm is proposed. Examples of loudspeaker-room modeling and equalization are presented. View full abstract»

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  • Comments on “Performance Analysis of Amplify-and-Forward Opportunistic Relaying in Rician Fading”

    Page(s): 142 - 143
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (238 KB)  

    It has been pointed out that the asymptotic SER formula of the opportunistic relaying derived by B. Maham is erroneous. The correct SER approximation is therefore presented. It is concluded that opportunistic relaying provides the coding gain in high SNR of R! times less as compared with the repetition-based cooperation. The simulations are also conducted to substantiate the corrected SER formula. View full abstract»

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  • Reply to “Comments on Performance Analysis of Amplify-and-Forward Opportunistic Relaying in Rician Fading”

    Page(s): 143
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    First, I would like to thank the associate editor and the author of the Comment for bringing this issue to our attention. While I agree with the mentioned comment, I think the error is rather trivial. The difference is only on a proportional constant for the derived asymptotic expression. Since the main contributions of our paper were the SER expressions in Section III-A and the diversity analysis of the system in Section III-B, the main results are correct. Moreover, in our paper, we used an asymptotic formula based on [Ribeiro et al., 2005, eq. (10)]. The authors in [Ribeiro et al.] also considered the case of M-QAM and M-PSK (see the footnote in p. 1265 of [Ribeiro et al.]), and thus, the expression we used can be a good approximation without multiplying with the constant "c". View full abstract»

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  • Comments on "OFDM Transmission for Time-Based Range Estimation"

    Page(s): 144
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    The letter by Wang and Fattouche makes the claim that the Cramer-Rao lower bound (CRLB) for range estimation using orthogonal frequency division multiplexing (OFDM) signals is 4.8 dB lower than that of pseudo-noise (PN) signals. However, this is not the case. The sole reason for the 4.8 dB gain in the said letter is due to the fact that the authors implicitly used an uncommon pulse shaping filter in the digital-to-analog conversion process for the PN signal. View full abstract»

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  • Blank page

    Page(s): 145 - 146
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  • IEEE Signal Processing Letters Information for authors

    Page(s): 147 - 148
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  • IEEE copyright form

    Page(s): 149 - 150
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  • IEEE Signal Processing Society Information

    Page(s): C3
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Aims & Scope

The IEEE Signal Processing Letters is a monthly, archival publication designed to provide rapid dissemination of original, cutting-edge ideas and timely, significant contributions in signal, image, speech, language and audio processing.

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Meet Our Editors

Editor-in-Chief
Peter Willett
University of Connecticut
Storrs, CT 06269
peter.willett@uconn.edu