Biometrics are quantities obtained from analyses of biological measurements. For human based biometrics, the two main types are clinical and authentication. This paper presents a brief comparison between the two, showing that on many occasions clinical biometrics can motivate for its use in authentication applications. Since several clinical biometrics deal with temporal data and also involve several dimensions of movement, we also present a new application of Singular Spectrum Analysis, in particular its multivariate version, to obtain significant frequency information across these dimensions. We use the most significant frequency component as a biometric to distinguish between various types of human movements. The signals were collected from triaxial accelerometers mounted in an object that is handled by a user. Although this biometric was obtained in a clinical setting, it shows promise for authentication.
{"title":"A multivariate Singular Spectrum Analysis approach to clinically-motivated movement biometrics","authors":"T. Lee, S. Gan, J. G. Lim, S. Sanei","doi":"10.5281/ZENODO.43824","DOIUrl":"https://doi.org/10.5281/ZENODO.43824","url":null,"abstract":"Biometrics are quantities obtained from analyses of biological measurements. For human based biometrics, the two main types are clinical and authentication. This paper presents a brief comparison between the two, showing that on many occasions clinical biometrics can motivate for its use in authentication applications. Since several clinical biometrics deal with temporal data and also involve several dimensions of movement, we also present a new application of Singular Spectrum Analysis, in particular its multivariate version, to obtain significant frequency information across these dimensions. We use the most significant frequency component as a biometric to distinguish between various types of human movements. The signals were collected from triaxial accelerometers mounted in an object that is handled by a user. Although this biometric was obtained in a clinical setting, it shows promise for authentication.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121035122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We discuss a few promising applications of the faster-than-Nyquist (FTN) signaling technique. Although proposed in the mid 70s, thanks to recent extensions this technique is taking on a new lease of life. In particular, we will discuss its applications to satellite systems for broadcasting transmissions, optical long-haul transmissions, and next-generation cellular systems, possibly equipped with a large scale antenna system (LSAS) at the base stations (BSs). Moreover, based on measurements with a 128 element antenna array, we analyze the spectral efficiency that can be achieved with simple receiver solutions in single carrier LSAS systems.
{"title":"Faster-than-Nyquist signaling for next generation communication architectures","authors":"Andrea Modenini, F. Rusek, G. Colavolpe","doi":"10.5281/ZENODO.44213","DOIUrl":"https://doi.org/10.5281/ZENODO.44213","url":null,"abstract":"We discuss a few promising applications of the faster-than-Nyquist (FTN) signaling technique. Although proposed in the mid 70s, thanks to recent extensions this technique is taking on a new lease of life. In particular, we will discuss its applications to satellite systems for broadcasting transmissions, optical long-haul transmissions, and next-generation cellular systems, possibly equipped with a large scale antenna system (LSAS) at the base stations (BSs). Moreover, based on measurements with a 128 element antenna array, we analyze the spectral efficiency that can be achieved with simple receiver solutions in single carrier LSAS systems.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125154280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as “regret”, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case.
{"title":"Comprehensive lower bounds on sequential prediction","authors":"N. D. Vanli, M. O. Sayin, S. Ergüt, S. Kozat","doi":"10.5281/ZENODO.44015","DOIUrl":"https://doi.org/10.5281/ZENODO.44015","url":null,"abstract":"We study the problem of sequential prediction of real-valued sequences under the squared error loss function. While refraining from any statistical and structural assumptions on the underlying sequence, we introduce a competitive approach to this problem and compare the performance of a sequential algorithm with respect to the large and continuous class of parametric predictors. We define the performance difference between a sequential algorithm and the best parametric predictor as “regret”, and introduce a guaranteed worst-case lower bounds to this relative performance measure. In particular, we prove that for any sequential algorithm, there always exists a sequence for which this regret is lower bounded by zero. We then extend this result by showing that the prediction problem can be transformed into a parameter estimation problem if the class of parametric predictors satisfy a certain property, and provide a comprehensive lower bound to this case.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131179765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
High mobility of terminals constitutes a hot topic that is commonly envisaged for the next Fifth Generation (5G) of mobile communication systems. The wireless propagation channel is a time-frequency variant. This aspect can dramatically damage the waveforms orthogonality that is induced in the Orthogonal frequency division multiplexing (OFDM) signal. Consequently, this results in oppressive Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI), which leads to performance degradation in OFDM systems. To efficiently overcome these drawbacks, we developed in [1] an adequate algorithm that maximizes the received Signal to Interference plus Noise Ratio (SINR) by optimizing systematically the OFDM waveforms at the Transmitter (TX) and Receiver (RX) sides. In this paper, we go further by investigating the performance evaluation of this algorithm. We start by testing its robustness against time and frequency synchronization errors. Then, as this algorithm banks on an iterative approach to find the optimal waveforms, we study the impact of the waveform initialization on its convergence. The obtained simulation results confirm the efficiency of this algorithm and its robustness compared to the conventional OFDM schemes, which makes it an appropriate good candidate for 5G systems.
{"title":"Numerical characterization for optimal designed waveform to multicarrier systems in 5G","authors":"Zeineb Hraiech, M. Siala, F. Abdelkefi","doi":"10.5281/ZENODO.43999","DOIUrl":"https://doi.org/10.5281/ZENODO.43999","url":null,"abstract":"High mobility of terminals constitutes a hot topic that is commonly envisaged for the next Fifth Generation (5G) of mobile communication systems. The wireless propagation channel is a time-frequency variant. This aspect can dramatically damage the waveforms orthogonality that is induced in the Orthogonal frequency division multiplexing (OFDM) signal. Consequently, this results in oppressive Inter-Carrier Interference (ICI) and Inter-Symbol Interference (ISI), which leads to performance degradation in OFDM systems. To efficiently overcome these drawbacks, we developed in [1] an adequate algorithm that maximizes the received Signal to Interference plus Noise Ratio (SINR) by optimizing systematically the OFDM waveforms at the Transmitter (TX) and Receiver (RX) sides. In this paper, we go further by investigating the performance evaluation of this algorithm. We start by testing its robustness against time and frequency synchronization errors. Then, as this algorithm banks on an iterative approach to find the optimal waveforms, we study the impact of the waveform initialization on its convergence. The obtained simulation results confirm the efficiency of this algorithm and its robustness compared to the conventional OFDM schemes, which makes it an appropriate good candidate for 5G systems.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121643668","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Carvalho, M. P. Sousa, J. V. S. Filho, J. S. Rocha, W. Lopes, M. Alencar
Cognitive radio is one of the most promising techniques of wireless communications, due to its many applications. Cognitive networks have the capability to congregate different cognitive users via cooperative spectrum sensing. Examples of cognitive networks can be found in important and different applications, such as digital television and wireless sensor networks. The objective of this paper is to analyze how signal processing techniques are used to provide reliable performance in such networks. Applications of signal processing in cognitive networks are presented and detailed.
{"title":"Signal processing applications for cognitive networks: State of the art","authors":"F. Carvalho, M. P. Sousa, J. V. S. Filho, J. S. Rocha, W. Lopes, M. Alencar","doi":"10.5281/ZENODO.54514","DOIUrl":"https://doi.org/10.5281/ZENODO.54514","url":null,"abstract":"Cognitive radio is one of the most promising techniques of wireless communications, due to its many applications. Cognitive networks have the capability to congregate different cognitive users via cooperative spectrum sensing. Examples of cognitive networks can be found in important and different applications, such as digital television and wireless sensor networks. The objective of this paper is to analyze how signal processing techniques are used to provide reliable performance in such networks. Applications of signal processing in cognitive networks are presented and detailed.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115705354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An adaptive waveform selection algorithm for target tracking by multistatic radar/sonar systems in wideband environments is presented to minimize the tracking mean squared error. The proposed selection algorithm is developed based on the minimization of the trace of error covariance matrix for the target state estimates (i.e. the target position and target velocity). This covariance matrix can be computed using the Cramér-Rao lower bounds of the wideband radar/sonar measurements. The performance advantage of the proposed adaptive waveform selection algorithm over the conventional fixed waveforms with minimum and maximum time-bandwidth products is demonstrated by simulation examples using various FM waveform classes.
{"title":"Adaptive waveform selection and target tracking by wideband multistatic radar/sonar systems","authors":"Ngoc Hung Nguyen, K. Doğançay, L. Davis","doi":"10.5281/ZENODO.43859","DOIUrl":"https://doi.org/10.5281/ZENODO.43859","url":null,"abstract":"An adaptive waveform selection algorithm for target tracking by multistatic radar/sonar systems in wideband environments is presented to minimize the tracking mean squared error. The proposed selection algorithm is developed based on the minimization of the trace of error covariance matrix for the target state estimates (i.e. the target position and target velocity). This covariance matrix can be computed using the Cramér-Rao lower bounds of the wideband radar/sonar measurements. The performance advantage of the proposed adaptive waveform selection algorithm over the conventional fixed waveforms with minimum and maximum time-bandwidth products is demonstrated by simulation examples using various FM waveform classes.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114100736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In a previous work [1], we have shown that model interpolation can be applied for acoustic model adaptation for a specific show. Compared to other approaches, this method has the advantage to be highly flexible, allowing rapid adaptation by simply reassigning the interpolation coefficients. In this work this approach is used for a multi-accented English broadcast news data recognition, which can be considered an arduous task due to the impact of accent variability on the recognition performance. The work described in [1] is extended in two ways. First, in order to reduce the parameters of the interpolated model, a theoretically motivated EM-like mixture reduction algorithm is proposed. Second, beyond supervised adaptation, model interpolation is used as an unsupervised adaptation framework, where the interpolation coefficients are estimated on-the-fly for each test segment.
{"title":"Speech recognition of multiple accented English data using acoustic model interpolation","authors":"Thiago Fraga-Silva, J. Gauvain, L. Lamel","doi":"10.5281/ZENODO.44197","DOIUrl":"https://doi.org/10.5281/ZENODO.44197","url":null,"abstract":"In a previous work [1], we have shown that model interpolation can be applied for acoustic model adaptation for a specific show. Compared to other approaches, this method has the advantage to be highly flexible, allowing rapid adaptation by simply reassigning the interpolation coefficients. In this work this approach is used for a multi-accented English broadcast news data recognition, which can be considered an arduous task due to the impact of accent variability on the recognition performance. The work described in [1] is extended in two ways. First, in order to reduce the parameters of the interpolated model, a theoretically motivated EM-like mixture reduction algorithm is proposed. Second, beyond supervised adaptation, model interpolation is used as an unsupervised adaptation framework, where the interpolation coefficients are estimated on-the-fly for each test segment.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117101652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, we examine the descriptiveness and recognition properties of different feature representations for the analysis of musical signals, aiming in the exploration of their microand macro-structures, for the task of music genre classification. We explore nonlinear methods, such as the AM-FM model and ideas from fractal theory, so as to model the time-varying harmonic structure of musical signals and the geometrical complexity of the music waveform. The different feature representations' efficacy is compared regarding their recognition properties for the specific task. The proposed features are evaluated against and in combination with Mel frequency cepstral coefficients (MFCC), using both static and dynamic classifiers, accomplishing an error reduction of 28%, illustrating that they can capture important aspects of music.
{"title":"Comparison of different representations based on nonlinear features for music genre classification","authors":"Athanasia Zlatintsi, P. Maragos","doi":"10.5281/ZENODO.44161","DOIUrl":"https://doi.org/10.5281/ZENODO.44161","url":null,"abstract":"In this paper, we examine the descriptiveness and recognition properties of different feature representations for the analysis of musical signals, aiming in the exploration of their microand macro-structures, for the task of music genre classification. We explore nonlinear methods, such as the AM-FM model and ideas from fractal theory, so as to model the time-varying harmonic structure of musical signals and the geometrical complexity of the music waveform. The different feature representations' efficacy is compared regarding their recognition properties for the specific task. The proposed features are evaluated against and in combination with Mel frequency cepstral coefficients (MFCC), using both static and dynamic classifiers, accomplishing an error reduction of 28%, illustrating that they can capture important aspects of music.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123289086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents a novel approach for room reconstruction using unknown sound signals generated in different locations of the environment. The approach is very general, that is fully uncalibrated, i.e. the locations of microphones, sound events and room reflectors are not known a priori. We show that, even if this problem implies a highly non-linear cost function, it is still possible to provide a solution close to the global minimum. Synthetic experiments show the proposed optimization framework can achieve reasonable results even in the presence of signal noise.
{"title":"Towards fully uncalibrated room reconstruction with sound","authors":"M. Crocco, A. Trucco, Vittorio Murino, A. D. Bue","doi":"10.5281/ZENODO.43892","DOIUrl":"https://doi.org/10.5281/ZENODO.43892","url":null,"abstract":"This paper presents a novel approach for room reconstruction using unknown sound signals generated in different locations of the environment. The approach is very general, that is fully uncalibrated, i.e. the locations of microphones, sound events and room reflectors are not known a priori. We show that, even if this problem implies a highly non-linear cost function, it is still possible to provide a solution close to the global minimum. Synthetic experiments show the proposed optimization framework can achieve reasonable results even in the presence of signal noise.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124771186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stochastic optimization plays an important role in solving many problems encountered in machine learning or adaptive processing. In this context, the second-order statistics of the data are often unknown a priori or their direct computation is too intensive, and they have to be estimated on-line from the related signals. In the context of batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g. a sparsity promoting function), Majorize-Minimize (MM) subspace methods have recently attracted much interest since they are fast, highly flexible and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the cost function is replaced by a sequence of stochastic approximations of it. Simulation results illustrate the good practical performance of the proposed MM Memory Gradient (3MG) algorithm when applied to 2D filter identification.
{"title":"A stochastic 3MG algorithm with application to 2D filter identification","authors":"É. Chouzenoux, J. Pesquet, A. Florescu","doi":"10.5281/ZENODO.44156","DOIUrl":"https://doi.org/10.5281/ZENODO.44156","url":null,"abstract":"Stochastic optimization plays an important role in solving many problems encountered in machine learning or adaptive processing. In this context, the second-order statistics of the data are often unknown a priori or their direct computation is too intensive, and they have to be estimated on-line from the related signals. In the context of batch optimization of an objective function being the sum of a data fidelity term and a penalization (e.g. a sparsity promoting function), Majorize-Minimize (MM) subspace methods have recently attracted much interest since they are fast, highly flexible and effective in ensuring convergence. The goal of this paper is to show how these methods can be successfully extended to the case when the cost function is replaced by a sequence of stochastic approximations of it. Simulation results illustrate the good practical performance of the proposed MM Memory Gradient (3MG) algorithm when applied to 2D filter identification.","PeriodicalId":198408,"journal":{"name":"2014 22nd European Signal Processing Conference (EUSIPCO)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121752326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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