Two natural competitors in the area of narrow-band spectrum analysis, namely the Chirp Z-transform (CZT) and the Generalized Goertzel algorithm (GGA), are taken and compared, with the focus on the computational cost. We present results showing that for real-input data, the GGA is preferable over the CZT in a range of practical situations. This is shown both in theory and in practice.
{"title":"Computational cost of Chirp Z-transform and Generalized Goertzel algorithm","authors":"P. Rajmic, Zdeněk Průša, Christoph Wiesmeyr","doi":"10.5281/ZENODO.44055","DOIUrl":"https://doi.org/10.5281/ZENODO.44055","url":null,"abstract":"Two natural competitors in the area of narrow-band spectrum analysis, namely the Chirp Z-transform (CZT) and the Generalized Goertzel algorithm (GGA), are taken and compared, with the focus on the computational cost. We present results showing that for real-input data, the GGA is preferable over the CZT in a range of practical situations. This is shown both in theory and in practice.","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":"114806320","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}
Sam Karimian-Azari, J. Benesty, J. Jensen, M. G. Christensen
The minimum variance distortionless response (MVDR) and the linearly constrained minimum variance (LCMV) beamformers are two optimal approaches in the sense of noise reduction. The LCMV beamformer can also reject interferers using linear constraints at the expense of reducing the degree of freedom in a limited number of microphones. However, it may magnify noise that causes a lower output signal-to-noise ratio (SNR) than the MVDR beamformer. Contrarily, the MVDR beamformer suffers from interference in output. In this paper, we propose a controllable LCMV (C-LCMV) beamformer based on the principles of both the MVDR and LCMV beamformers. The C-LCMV approach can control a compromise between noise reduction and interference rejection. Simulation results show that the C-LCMV beamformer outperforms the MVDR beamformer in interference rejection, and the LCMV beamformer in background noise reduction.
{"title":"A broadband beamformer using controllable constraints and minimum variance","authors":"Sam Karimian-Azari, J. Benesty, J. Jensen, M. G. Christensen","doi":"10.5281/ZENODO.43949","DOIUrl":"https://doi.org/10.5281/ZENODO.43949","url":null,"abstract":"The minimum variance distortionless response (MVDR) and the linearly constrained minimum variance (LCMV) beamformers are two optimal approaches in the sense of noise reduction. The LCMV beamformer can also reject interferers using linear constraints at the expense of reducing the degree of freedom in a limited number of microphones. However, it may magnify noise that causes a lower output signal-to-noise ratio (SNR) than the MVDR beamformer. Contrarily, the MVDR beamformer suffers from interference in output. In this paper, we propose a controllable LCMV (C-LCMV) beamformer based on the principles of both the MVDR and LCMV beamformers. The C-LCMV approach can control a compromise between noise reduction and interference rejection. Simulation results show that the C-LCMV beamformer outperforms the MVDR beamformer in interference rejection, and the LCMV beamformer in background noise reduction.","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":"115046381","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}
Sebastian Schmale, J. Hoeffmann, Benjamin Knoop, G. Kreiselmeyer, H. Hamer, D. Peters-Drolshagen, S. Paul
Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.
{"title":"Exploiting correlation in neural signals for data compression","authors":"Sebastian Schmale, J. Hoeffmann, Benjamin Knoop, G. Kreiselmeyer, H. Hamer, D. Peters-Drolshagen, S. Paul","doi":"10.5281/ZENODO.43887","DOIUrl":"https://doi.org/10.5281/ZENODO.43887","url":null,"abstract":"Progress in invasive brain research relies on signal acquisition at high temporal- and spatial resolutions, resulting in a data deluge at the (wireless) interface to the external world. Hence, data compression at the implant site is necessary in order to comply with the neurophysiological restrictions, especially when it comes to recording and transmission of neural raw data. This work investigates spatial correlations of neural signals, leading to a significant increase in data compression with a suitable sparse signal representation before the wireless data transmission at the implant site. Subsequently, we used the correlation-aware two-dimensional DCT used in image processing, to exploit spatial correlation of the data set. In order to guarantee a certain sparsity in the signal representation, two paradigms of zero forcing are evaluated and applied: Significant coefficients- and block sparsity-zero forcing.","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":"131918488","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 proposes the use of a deep neural network for the recognition of isolated acoustic events such as footsteps, baby crying, motorcycle, rain etc. For an acoustic event classification task containing 61 distinct classes, classification accuracy of the neural network classifier (60.3%) excels that of the conventional Gaussian mixture model based hidden Markov model classifier (54.8%). In addition, an unsupervised layer-wise pretraining followed by standard backpropagation training of a deep network (known as a deep belief network) results in further increase of 2-4% in classification accuracy. Effects of implementation parameters such as types of features and number of adjacent frames as additional features are found to be significant on classification accuracy.
{"title":"Recognition of acoustic events using deep neural networks","authors":"O. Gencoglu, T. Virtanen, H. Huttunen","doi":"10.5281/ZENODO.43987","DOIUrl":"https://doi.org/10.5281/ZENODO.43987","url":null,"abstract":"This paper proposes the use of a deep neural network for the recognition of isolated acoustic events such as footsteps, baby crying, motorcycle, rain etc. For an acoustic event classification task containing 61 distinct classes, classification accuracy of the neural network classifier (60.3%) excels that of the conventional Gaussian mixture model based hidden Markov model classifier (54.8%). In addition, an unsupervised layer-wise pretraining followed by standard backpropagation training of a deep network (known as a deep belief network) results in further increase of 2-4% in classification accuracy. Effects of implementation parameters such as types of features and number of adjacent frames as additional features are found to be significant on classification accuracy.","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":"132246081","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}
A. Boudjellal, K. Abed-Meraim, A. Belouchrani, P. Ravier
This paper deals with the separation problem of dependent sources. The separation is made possible thanks to side information on the dependence nature of the considered sources. In this work, we first show how this side information can be used to achieve desired source separation using joint matrix decomposition techniques. Indeed, in the case of statistically independent sources, many BSS methods are based on joint matrix diagonalization. In our case, we replace the target diagonal structure by appropriate non diagonal one which reflects the dependence nature of the sources. This new concept is illustrated with two simple 2×2 source separation exampleswhere second-order-statistics and high-order-statistics are used respectively.
{"title":"Informed separation of dependent sources using joint matrix decomposition","authors":"A. Boudjellal, K. Abed-Meraim, A. Belouchrani, P. Ravier","doi":"10.5281/ZENODO.44112","DOIUrl":"https://doi.org/10.5281/ZENODO.44112","url":null,"abstract":"This paper deals with the separation problem of dependent sources. The separation is made possible thanks to side information on the dependence nature of the considered sources. In this work, we first show how this side information can be used to achieve desired source separation using joint matrix decomposition techniques. Indeed, in the case of statistically independent sources, many BSS methods are based on joint matrix diagonalization. In our case, we replace the target diagonal structure by appropriate non diagonal one which reflects the dependence nature of the sources. This new concept is illustrated with two simple 2×2 source separation exampleswhere second-order-statistics and high-order-statistics are used respectively.","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":"128565811","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}
Ivo Soares, M. Castelo‐Branco, António M. G. Pinheiro
This paper describes a new method for the calculation of the retinal vessel centerlines using a scale-space approach for an increased reliability and effectiveness. The algorithm begins with a new vessel detector description method based on a modified corner detector. Then the vessel detector image is filtered with a set of binary rotating filters, resulting in enhanced vessels structures. The main vessels can be selected with a dynamic thresholding approach. In order to deal with vessels bifurcations and vessels crossovers that might not be detected, the initial retinal image is processed with a set of four directional differential operators. The resulting directional images are then combined with the detected vessels, creating the final vessels centerlines image. The performance of the algorithm is evaluated using two different datasets.
{"title":"Vessel centerline detection in retinal images based on a corner detector and dynamic thresholding","authors":"Ivo Soares, M. Castelo‐Branco, António M. G. Pinheiro","doi":"10.5281/ZENODO.44200","DOIUrl":"https://doi.org/10.5281/ZENODO.44200","url":null,"abstract":"This paper describes a new method for the calculation of the retinal vessel centerlines using a scale-space approach for an increased reliability and effectiveness. The algorithm begins with a new vessel detector description method based on a modified corner detector. Then the vessel detector image is filtered with a set of binary rotating filters, resulting in enhanced vessels structures. The main vessels can be selected with a dynamic thresholding approach. In order to deal with vessels bifurcations and vessels crossovers that might not be detected, the initial retinal image is processed with a set of four directional differential operators. The resulting directional images are then combined with the detected vessels, creating the final vessels centerlines image. The performance of the algorithm is evaluated using two different datasets.","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":"133200310","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}
Samil Karahan, Adil Karaoz, O. F. Ozdemir, Ahmet Gokhan Gu, U. Uludag
We concentrate on utilization of facial periocular region for biometric identification. Although this region has superior discriminative characteristics, as compared to mouth and nose, it has not been frequently used as an independent modality for personal identification. We employ a feature-based representation, where the associated periocular image is divided into left and right sides, and descriptor vectors are extracted from these using popular feature extraction algorithms SIFT, SURF, BRISK, ORB, and LBP. We also concatenate descriptor vectors. Utilizing FLANN and Brute Force matchers, we report recognition rates and ROC. For the periocular region image data, obtained from widely used FERET database consisting of 865 subjects, we obtain Rank-1 recognition rate of 96.8% for full frontal and different facial expressions in same session cases. We include a summary of existing methods, and show that the proposed method produces lower/comparable error rates with respect to the current state of the art.
{"title":"On identification from periocular region utilizing SIFT and SURF","authors":"Samil Karahan, Adil Karaoz, O. F. Ozdemir, Ahmet Gokhan Gu, U. Uludag","doi":"10.5281/ZENODO.44187","DOIUrl":"https://doi.org/10.5281/ZENODO.44187","url":null,"abstract":"We concentrate on utilization of facial periocular region for biometric identification. Although this region has superior discriminative characteristics, as compared to mouth and nose, it has not been frequently used as an independent modality for personal identification. We employ a feature-based representation, where the associated periocular image is divided into left and right sides, and descriptor vectors are extracted from these using popular feature extraction algorithms SIFT, SURF, BRISK, ORB, and LBP. We also concatenate descriptor vectors. Utilizing FLANN and Brute Force matchers, we report recognition rates and ROC. For the periocular region image data, obtained from widely used FERET database consisting of 865 subjects, we obtain Rank-1 recognition rate of 96.8% for full frontal and different facial expressions in same session cases. We include a summary of existing methods, and show that the proposed method produces lower/comparable error rates with respect to the current state of the art.","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":"125718698","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}
L. Baltar, Tobias Laas, M. Newinger, A. Mezghani, J. Nossek
Advanced multicarrier systems, like the Offset-QAM filter bank based (OQAM-FBMC) ones, are gaining importance as candidates for the physical layer of the 5-th generation of wireless communications. One of the main advantages of FBMC, when compared to traditional cyclic prefix based OFDM, is its higher spectral efficiency. However, this gain can be lost again if the problem of training based channel estimation is not tackled correctly. This is due to the memory inserted by the longer pulse shaping and the loss of orthogonality of overlapping subcarriers. In this paper we approach the problem of training based channel estimation for FBMC systems. We propose an iterative algorithm based on the expectation maximization (EM) maximum likelihood (ML) that reduces the overhead and consequently improves the spectral efficiency.
{"title":"Enhancing spectral efficiency in advanced multicarrier techniques: A challenge","authors":"L. Baltar, Tobias Laas, M. Newinger, A. Mezghani, J. Nossek","doi":"10.5281/ZENODO.43846","DOIUrl":"https://doi.org/10.5281/ZENODO.43846","url":null,"abstract":"Advanced multicarrier systems, like the Offset-QAM filter bank based (OQAM-FBMC) ones, are gaining importance as candidates for the physical layer of the 5-th generation of wireless communications. One of the main advantages of FBMC, when compared to traditional cyclic prefix based OFDM, is its higher spectral efficiency. However, this gain can be lost again if the problem of training based channel estimation is not tackled correctly. This is due to the memory inserted by the longer pulse shaping and the loss of orthogonality of overlapping subcarriers. In this paper we approach the problem of training based channel estimation for FBMC systems. We propose an iterative algorithm based on the expectation maximization (EM) maximum likelihood (ML) that reduces the overhead and consequently improves the spectral efficiency.","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":"123660840","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 present a reduced complexity iterative grid-search technique for locating non-cooperating primary emitters in cognitive radio networks using received signal strength (RSS) measurements. The technique is based on dividing the search space into a smaller number of candidate subregions, selecting the best candidate that minimizes a cost function and repeating the process iteratively over the selections. We evaluate the performance of the proposed algorithm in independent shadowing scenarios and show that the performance closely approaches to that of the full search, particularly at small shadowing spread values with significantly reduced computational complexity. We also look at the performance of our algorithm when the initial search space is specified based on two different data-aided approaches using sensor measurements. Our simulation results show that the data-aided initialization schemes do not provide performance improvement over blind initialization.
{"title":"Iterative grid search for RSS-based emitter localization","authors":"Suzan Ureten, A. Yongaçoğlu, E. Petriu","doi":"10.5281/ZENODO.43974","DOIUrl":"https://doi.org/10.5281/ZENODO.43974","url":null,"abstract":"In this paper, we present a reduced complexity iterative grid-search technique for locating non-cooperating primary emitters in cognitive radio networks using received signal strength (RSS) measurements. The technique is based on dividing the search space into a smaller number of candidate subregions, selecting the best candidate that minimizes a cost function and repeating the process iteratively over the selections. We evaluate the performance of the proposed algorithm in independent shadowing scenarios and show that the performance closely approaches to that of the full search, particularly at small shadowing spread values with significantly reduced computational complexity. We also look at the performance of our algorithm when the initial search space is specified based on two different data-aided approaches using sensor measurements. Our simulation results show that the data-aided initialization schemes do not provide performance improvement over blind initialization.","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":"130422897","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 new variational Bayes algorithm for the adaptive estimation of signals possessing group structured sparsity. The proposed algorithm can be considered as an extension of a recently proposed variational Bayes framework of adaptive algorithms that utilize heavy tailed priors (such as the Student-t distribution) to impose sparsity. Variational inference is efficiently implemented via appropriate time recursive equations for all model parameters. Experimental results are provided that demonstrate the improved estimation performance of the proposed adaptive group sparse variational Bayes method, when compared to state-of-the-art sparse adaptive algorithms.
{"title":"Group-sparse adaptive variational Bayes estimation","authors":"K. Themelis, A. Rontogiannis, K. Koutroumbas","doi":"10.5281/ZENODO.44041","DOIUrl":"https://doi.org/10.5281/ZENODO.44041","url":null,"abstract":"This paper presents a new variational Bayes algorithm for the adaptive estimation of signals possessing group structured sparsity. The proposed algorithm can be considered as an extension of a recently proposed variational Bayes framework of adaptive algorithms that utilize heavy tailed priors (such as the Student-t distribution) to impose sparsity. Variational inference is efficiently implemented via appropriate time recursive equations for all model parameters. Experimental results are provided that demonstrate the improved estimation performance of the proposed adaptive group sparse variational Bayes method, when compared to state-of-the-art sparse adaptive algorithms.","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":"129115724","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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