We propose a method for vocal tract estimation that is better than Bozkurt's chirp group delay method [1] and its zero-phase variant [2]. The chirp group delay method works only for voiced speech, is critically dependent on finding the glottal closure instants (GCI), deteriorates in performance when more than two pitch cycles are included for analysis, and does not work for unvoiced speech. The zero-phase variant eliminates these drawbacks but works poorly for nasal sounds. In our proposed method all outside-unit-circle zeros are reflected inside before computing the chirp group delay. The advantages are: (a) GCI knowledge not required, (b) the vocal tract estimate is far less sensitive to the location and duration of the analysis window, (c) works for unvoiced sounds, and (d) captures the spectral valleys well for nasals, which in turn leads to better recognition accuracy.
{"title":"An improved chirp group delay based algorithm for estimating the vocal tract response","authors":"M. Jayesh, C. S. Ramalingam","doi":"10.5281/ZENODO.54522","DOIUrl":"https://doi.org/10.5281/ZENODO.54522","url":null,"abstract":"We propose a method for vocal tract estimation that is better than Bozkurt's chirp group delay method [1] and its zero-phase variant [2]. The chirp group delay method works only for voiced speech, is critically dependent on finding the glottal closure instants (GCI), deteriorates in performance when more than two pitch cycles are included for analysis, and does not work for unvoiced speech. The zero-phase variant eliminates these drawbacks but works poorly for nasal sounds. In our proposed method all outside-unit-circle zeros are reflected inside before computing the chirp group delay. The advantages are: (a) GCI knowledge not required, (b) the vocal tract estimate is far less sensitive to the location and duration of the analysis window, (c) works for unvoiced sounds, and (d) captures the spectral valleys well for nasals, which in turn leads to better recognition 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":"114069042","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 work, we propose a cross-layer design strategy based on a joint successive interference cancellation (SIC) detection technique and a multi-relay selection algorithm for the uplink of cooperative direct-sequence code-division multiple access (DS-CDMA) systems. We devise a low-cost greedy list-based SIC (GL-SIC) strategy with RAKE receivers as the front-end that can approach the maximum likelihood detector performance. We also present a low-complexity multi-relay selection algorithm based on greedy techniques that can approach the performance of an exhaustive search. Simulations show an excellent bit error rate performance of the proposed detection and relay selection algorithms as compared to existing techniques.
{"title":"Joint sic and multi-relay selection algorithms for cooperative DS-CDMA systems","authors":"Jiaqi Gu, R. D. Lamare","doi":"10.5281/ZENODO.43825","DOIUrl":"https://doi.org/10.5281/ZENODO.43825","url":null,"abstract":"In this work, we propose a cross-layer design strategy based on a joint successive interference cancellation (SIC) detection technique and a multi-relay selection algorithm for the uplink of cooperative direct-sequence code-division multiple access (DS-CDMA) systems. We devise a low-cost greedy list-based SIC (GL-SIC) strategy with RAKE receivers as the front-end that can approach the maximum likelihood detector performance. We also present a low-complexity multi-relay selection algorithm based on greedy techniques that can approach the performance of an exhaustive search. Simulations show an excellent bit error rate performance of the proposed detection and relay selection algorithms as compared to existing techniques.","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":"125985433","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}
Assuming a non-stationary Weibull background with no prior knowledge about the presence or not of a clutter edge, we propose and analyze the censoring and detection performances of the automatic censoring Weber-Haykin constant false censoring and alarm rates (ACWH-CFCAR) detector in homogeneous clutter and in the presence of a clutter edge within the reference window. The cfcarness property is assured by use of the Weber-Haykin (WH) adaptive thresholding which bypasses the estimation of the distribution parameters. The censoring algorithm starts up by considering the two most left ranked cells and proceeds forward. The selected homogeneous set is used to estimate the unknown background level. Extensive Monte Carlo simulations show that the performances of the proposed detector are similar to those exhibited by the corresponding fixed-point censoring WH-CFAR detector.
{"title":"Automatic WH-based edge detector in Weibull clutter","authors":"Souad Chabbi, T. Laroussi, A. Mezache","doi":"10.5281/ZENODO.43834","DOIUrl":"https://doi.org/10.5281/ZENODO.43834","url":null,"abstract":"Assuming a non-stationary Weibull background with no prior knowledge about the presence or not of a clutter edge, we propose and analyze the censoring and detection performances of the automatic censoring Weber-Haykin constant false censoring and alarm rates (ACWH-CFCAR) detector in homogeneous clutter and in the presence of a clutter edge within the reference window. The cfcarness property is assured by use of the Weber-Haykin (WH) adaptive thresholding which bypasses the estimation of the distribution parameters. The censoring algorithm starts up by considering the two most left ranked cells and proceeds forward. The selected homogeneous set is used to estimate the unknown background level. Extensive Monte Carlo simulations show that the performances of the proposed detector are similar to those exhibited by the corresponding fixed-point censoring WH-CFAR detector.","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":"126617602","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. Lavrenko, F. Roemer, G. D. Galdo, R. Thomä, O. Arikan
Compressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge of the sparsity level, i.e., the number of non-zero elements. However, the sparsity level is often not known a priori and can even vary with time. In this contribution we show that it is possible to estimate the sparsity level directly in the compressed domain, provided that multiple independent observations are available. In fact, one can use classical model order selection algorithms for this purpose. Nevertheless, due to the influence of the measurement process they may not perform satisfactorily in the compressed sensing setup. To overcome this drawback, we propose an approach which exploits the empirical distributions of the noise eigenvalues. We demonstrate its superior performance compared to state-of-the-art model order estimation algorithms numerically.
{"title":"An empirical eigenvalue-threshold test for sparsity level estimation from compressed measurements","authors":"A. Lavrenko, F. Roemer, G. D. Galdo, R. Thomä, O. Arikan","doi":"10.5281/ZENODO.44108","DOIUrl":"https://doi.org/10.5281/ZENODO.44108","url":null,"abstract":"Compressed sensing allows for a significant reduction of the number of measurements when the signal of interest is of a sparse nature. Most computationally efficient algorithms for signal recovery rely on some knowledge of the sparsity level, i.e., the number of non-zero elements. However, the sparsity level is often not known a priori and can even vary with time. In this contribution we show that it is possible to estimate the sparsity level directly in the compressed domain, provided that multiple independent observations are available. In fact, one can use classical model order selection algorithms for this purpose. Nevertheless, due to the influence of the measurement process they may not perform satisfactorily in the compressed sensing setup. To overcome this drawback, we propose an approach which exploits the empirical distributions of the noise eigenvalues. We demonstrate its superior performance compared to state-of-the-art model order estimation algorithms numerically.","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":"129111742","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 the field of underlay cognitive radio communications, the signal transmitted by the secondary user is disturbed by incoming signals from primary users. Thus, it is necessary to compensate for this secondary-link degradation at the receiver level. In this paper we use Dirichlet process mixtures (DPM) to relax a priori assumptions on the characteristics of the primary user-induced interference. DPM allow us to model the probability density function of the interference. The latter is estimated jointly with the symbols and the channel of the secondary link by using marginalized particle filtering. Our approach makes it possible to improve the symbol error rate compared with an algorithm that simply models the interference as a Gaussian noise.
{"title":"Relevance of Dirichlet process mixtures for modeling interferences in underlay cognitive radio","authors":"V. Pereira, G. Ferré, A. Giremus, É. Grivel","doi":"10.5281/ZENODO.44128","DOIUrl":"https://doi.org/10.5281/ZENODO.44128","url":null,"abstract":"In the field of underlay cognitive radio communications, the signal transmitted by the secondary user is disturbed by incoming signals from primary users. Thus, it is necessary to compensate for this secondary-link degradation at the receiver level. In this paper we use Dirichlet process mixtures (DPM) to relax a priori assumptions on the characteristics of the primary user-induced interference. DPM allow us to model the probability density function of the interference. The latter is estimated jointly with the symbols and the channel of the secondary link by using marginalized particle filtering. Our approach makes it possible to improve the symbol error rate compared with an algorithm that simply models the interference as a Gaussian 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":"127009163","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 new method for increasing the number of resolvable sources in direction-of-arrival estimation using co-prime arrays. This is achieved by utilizing multiple frequencies to fill in the missing elements in the difference coarray of the co-prime array corresponding to the reference frequency. For high signal-to-noise ratio, the multi-frequency approach effectively utilizes all of the degrees-of-freedom offered by the coarray, provided that the sources have proportional spectra. The performance of the proposed method is evaluated through numerical simulations.
{"title":"Direction-of-arrival estimation using multi-frequency co-prime arrays","authors":"Elie BouDaher, Yong Jia, F. Ahmad, M. Amin","doi":"10.5281/ZENODO.43952","DOIUrl":"https://doi.org/10.5281/ZENODO.43952","url":null,"abstract":"In this paper, we present a new method for increasing the number of resolvable sources in direction-of-arrival estimation using co-prime arrays. This is achieved by utilizing multiple frequencies to fill in the missing elements in the difference coarray of the co-prime array corresponding to the reference frequency. For high signal-to-noise ratio, the multi-frequency approach effectively utilizes all of the degrees-of-freedom offered by the coarray, provided that the sources have proportional spectra. The performance of the proposed method is evaluated through numerical simulations.","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":"116985530","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}
Reduction of the out-of-band (OOB) emission is essential for Cognitive Radio (CR) systems to enable coexistence with licensed (primary) systems operating in the adjacent frequency bands. This paper proposes an algorithm for the Non Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM)-based CR, to reduce the interference caused by both OOB radiation and by non-ideal frequency selectivity of a primary user (PU) receiver. It is based on a concept to use a set of subcarriers called Cancellation Carriers (CCs). By being aware of the PU's carrier frequency, the observed interference power can by decreased by about 10 dB in comparison with the standard OOB-power minimizing algorithms.
{"title":"Advanced interference reduction in NC-OFDM based Cognitive Radio with Cancellation Carriers","authors":"P. Kryszkiewicz, H. Bogucka","doi":"10.5281/ZENODO.43848","DOIUrl":"https://doi.org/10.5281/ZENODO.43848","url":null,"abstract":"Reduction of the out-of-band (OOB) emission is essential for Cognitive Radio (CR) systems to enable coexistence with licensed (primary) systems operating in the adjacent frequency bands. This paper proposes an algorithm for the Non Contiguous Orthogonal Frequency Division Multiplexing (NC-OFDM)-based CR, to reduce the interference caused by both OOB radiation and by non-ideal frequency selectivity of a primary user (PU) receiver. It is based on a concept to use a set of subcarriers called Cancellation Carriers (CCs). By being aware of the PU's carrier frequency, the observed interference power can by decreased by about 10 dB in comparison with the standard OOB-power minimizing 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":"132218872","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}
Super Gaussian (SG) distributions have proven to be very powerful prior models to induce sparsity in Bayesian Blind Deconvolution (BD) problems. Their conjugate based representations make them specially attractive when Variational Bayes (VB) inference is used since their variational parameters can be calculated in closed form with the sole knowledge of the energy function of the prior model. In this work we show how the introduction in the SG distribution of a global strength (not necessary scale) parameter can be used to improve the quality of the obtained restorations as well as to introduce additional information on the global weight of the prior. A model to estimate the new unknown parameter within the Bayesian framework is provided. Experimental results, on both synthetic and real images, demonstrate the effectiveness of the proposed approach.
{"title":"Parameter estimation in Bayesian Blind Deconvolution with super Gaussian image priors","authors":"M. Vega, R. Molina, A. Katsaggelos","doi":"10.5281/ZENODO.43886","DOIUrl":"https://doi.org/10.5281/ZENODO.43886","url":null,"abstract":"Super Gaussian (SG) distributions have proven to be very powerful prior models to induce sparsity in Bayesian Blind Deconvolution (BD) problems. Their conjugate based representations make them specially attractive when Variational Bayes (VB) inference is used since their variational parameters can be calculated in closed form with the sole knowledge of the energy function of the prior model. In this work we show how the introduction in the SG distribution of a global strength (not necessary scale) parameter can be used to improve the quality of the obtained restorations as well as to introduce additional information on the global weight of the prior. A model to estimate the new unknown parameter within the Bayesian framework is provided. Experimental results, on both synthetic and real images, demonstrate the effectiveness of the proposed approach.","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":"134470351","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}
Dumidu S. Talagala, Xiang Wu, Wen Zhang, T. Abhayapala
In binaural systems, source localization in the median plane is challenging due to the difficulty of exploring the spectral cues of the head-related transfer function (HRTF) independently of the source spectra. This paper presents a method of extracting the HRTF spectral cues using cepstral analysis for speech source localization in the median plane. Binaural signals are preprocessed in the cepstral domain so that the fine spectral structure of speech and the HRTF spectral envelope can be easily separated. We introduce (i) a truncated cepstral transformation to extract the relevant localization cues, and (ii) a mechanism to normalize the effects of the time varying speech spectra. The proposed method is evaluated and compared with a convolution based localization method using a speech corpus of multiple speakers. The results suggest that the proposed method fully exploits the available spectral cues for robust speaker independent binaural source localization in the median plane.
{"title":"Binaural localization of speech sources in the median plane using cepstral hrtf extraction","authors":"Dumidu S. Talagala, Xiang Wu, Wen Zhang, T. Abhayapala","doi":"10.5281/ZENODO.44021","DOIUrl":"https://doi.org/10.5281/ZENODO.44021","url":null,"abstract":"In binaural systems, source localization in the median plane is challenging due to the difficulty of exploring the spectral cues of the head-related transfer function (HRTF) independently of the source spectra. This paper presents a method of extracting the HRTF spectral cues using cepstral analysis for speech source localization in the median plane. Binaural signals are preprocessed in the cepstral domain so that the fine spectral structure of speech and the HRTF spectral envelope can be easily separated. We introduce (i) a truncated cepstral transformation to extract the relevant localization cues, and (ii) a mechanism to normalize the effects of the time varying speech spectra. The proposed method is evaluated and compared with a convolution based localization method using a speech corpus of multiple speakers. The results suggest that the proposed method fully exploits the available spectral cues for robust speaker independent binaural source localization in the median plane.","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":"131946007","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}
Compact Descriptors for Visual Search (CDVS) is MPEG proposed standard that will enable efficient and interoperable design of visual search applications using local descriptors. Such descriptors are invariant to rotation and scaling, but are not very robust towards viewpoint changes. In this paper, we address this problem and propose a modified version of the CDVS pipeline that employs image back-projection to compensate for perspective distortion. The proposed technique is based on the homography derived from the correspondence extracted from pairs of matching keypoints. Extensive results show that it improves the CDVS matching accuracy under viewpoint changes while having low complexity.
{"title":"A homography-based CDVS pipeline for image matchingwith improved resilience to viewpoint changes","authors":"Biao Zhao, E. Magli","doi":"10.5281/ZENODO.44189","DOIUrl":"https://doi.org/10.5281/ZENODO.44189","url":null,"abstract":"Compact Descriptors for Visual Search (CDVS) is MPEG proposed standard that will enable efficient and interoperable design of visual search applications using local descriptors. Such descriptors are invariant to rotation and scaling, but are not very robust towards viewpoint changes. In this paper, we address this problem and propose a modified version of the CDVS pipeline that employs image back-projection to compensate for perspective distortion. The proposed technique is based on the homography derived from the correspondence extracted from pairs of matching keypoints. Extensive results show that it improves the CDVS matching accuracy under viewpoint changes while having low complexity.","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":"123022221","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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