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2013 IEEE International Conference on Acoustics, Speech and Signal Processing最新文献

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Empirical likelihood ratio test with density function constraints 具有密度函数约束的经验似然比检验
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638886
Yingxi Liu, A. Tewfik
{"title":"Empirical likelihood ratio test with density function constraints","authors":"Yingxi Liu, A. Tewfik","doi":"10.1109/ICASSP.2013.6638886","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638886","url":null,"abstract":"In this work, we study non-parametric hypothesis testing problem with density function constraints. The empirical likelihood ratio test has been widely used in testing problems with moment (in)equality constraints. However, some detection problems cannot be described using moment (in)equalities. We propose a density function constraint along with an empirical likelihood ratio test. This detector is applicable to a wide variety of robust parametric/non-parametric detection problems. Since the density function constraints provide a more exact description of the null hypothesis, the test outperforms many other alternatives such as the empirical likelihood ratio test with moment constraints and robust Kolmogorov-Smirnov test, especially when the alternative hypothesis has a special structure.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"26 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120997775","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}
引用次数: 0
Wavelength-adaptive image formation model and geometric classification for defogging unmanned aerial vehicle images 波长自适应图像形成模型及无人机图像去雾几何分类
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638096
Inhye Yoon, M. Hayes, J. Paik
{"title":"Wavelength-adaptive image formation model and geometric classification for defogging unmanned aerial vehicle images","authors":"Inhye Yoon, M. Hayes, J. Paik","doi":"10.1109/ICASSP.2013.6638096","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638096","url":null,"abstract":"In this paper, we present an image enhancement algorithm based on the wavelength-adaptive image formation model and geometric classification for defogging UAV images. We first generate a labeled image using geometric class-based segmentation. We then generate a modified transmission map based on the wavelength-adaptive image formation model with scattering coefficients in the labeled image. We also estimate the atmospheric light from the modified transmission map instead of simply choosing the brightest pixel. The proposed method can significantly enhance the visibility of foggy UAV images compared with existing monochrome model-based defogging method. The proposed algorithm can enhance the visibility by removing atmospheric degradation factor in airborne images acquired by aerial platforms such as satellite, airplane, and UAV under critical weather conditions such as haze, fog, and smoke.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116003581","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}
引用次数: 4
A non-negative sparse promoting algorithm for high resolution hyperspectral imaging 高分辨率高光谱成像的非负稀疏提升算法
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6637883
Eliot Wycoff, Tsung-Han Chan, K. Jia, Wing-Kin Ma, Yi Ma
{"title":"A non-negative sparse promoting algorithm for high resolution hyperspectral imaging","authors":"Eliot Wycoff, Tsung-Han Chan, K. Jia, Wing-Kin Ma, Yi Ma","doi":"10.1109/ICASSP.2013.6637883","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6637883","url":null,"abstract":"Promoting the spatial resolution of off-the-shelf hyperspectral sensors is expected to improve typical computer vision tasks, such as target tracking and image classification. In this paper, we investigate the scenario in which two cameras, one with a conventional RGB sensor and the other with a hyperspectral sensor, capture the same scene, attempting to extract redundant and complementary information. We propose a non-negative sparse promoting framework to integrate the hyperspectral and RGB data into a high resolution hyperspectral set of data. The formulated problem is in the form of a sparse non-negative matrix factorization with prior knowledge on the spectral and spatial transform responses, and it can be handled by alternating optimization where each subproblem is solved by efficient convex optimization solvers; e.g., the alternating direction method of multipliers. Experiments on a public database show that our method achieves much lower average reconstruction errors than other state-of-the-art methods.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124548697","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}
引用次数: 130
Methods for classification of nocturnal migratory bird vocalizations using Pseudo Wigner-Ville Transform 基于伪Wigner-Ville变换的夜间候鸟叫声分类方法
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6637750
Anand Patti, G. Williamson
{"title":"Methods for classification of nocturnal migratory bird vocalizations using Pseudo Wigner-Ville Transform","authors":"Anand Patti, G. Williamson","doi":"10.1109/ICASSP.2013.6637750","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6637750","url":null,"abstract":"Many species of birds in Americas vocalize during nocturnal migration flights. Acoustic detection and classification of the calls show potential for study of the natural history of these migrant birds. In particular, information about the species' composition and number of birds involved in migration movements may be obtainable through acoustic techniques. Other methods such as radar monitoring may have capability only to assess the number, but not the composition. Mel Frequency Cepstral Coefficients-Gaussian Mixture Model-based methods (MFCC-GMM), Mel Frequency Cepstral Coefficients-Hidden Markov Model-based methods (MFCC-HMM) and spectrogram correlation-based methods have been proposed to automate the recognition/classification of the nocturnal flight calls. Here we investigate the choice of Pseudo Wigner-Ville Transform (PWVT) on MFCC-HMM-based classifier and correlation-based classifier performance. We use a collection of recordings of nocturnal flight calls of several species of thrushes and other bird species with similar calls to evaluate and compare classifiers.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124526412","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}
引用次数: 13
A waveform covariancematrix for high SINR and lowside-lobe levels 高信噪比和低旁瓣电平的波形协方差矩阵
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638429
Sajid Ahmed, Mohamed-Slim Alouini
{"title":"A waveform covariancematrix for high SINR and lowside-lobe levels","authors":"Sajid Ahmed, Mohamed-Slim Alouini","doi":"10.1109/ICASSP.2013.6638429","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638429","url":null,"abstract":"In this work to exploit the benefits of both multiple-input multiple-output (MIMO)-radar and phased-array a waveform covariance matrix is proposed. Our analytical results show that the proposed covariance matrix yields gain in signal-to-interference-plus-noise ratio (SINR) compared to MIMO-radar while the gain in SINR is close to phased-array and recently proposed phased-MIMO scheme. Transmitted waveforms with the proposed covariance matrix, at the receiver, significantly supress the side-lobe levels compared to phased-array, MIMO-radar, and phased-MIMO schemes. Moreover, in contrast to phased-MIMO our proposed scheme allows same power transmission from each antenna. Simulation results validate the analytical results.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115078408","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}
引用次数: 0
Joint recovery of sparse signals and parameter perturbations with parameterized measurement models 基于参数化测量模型的稀疏信号和参数扰动联合恢复
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638796
Erik C. Johnson, Douglas L. Jones
{"title":"Joint recovery of sparse signals and parameter perturbations with parameterized measurement models","authors":"Erik C. Johnson, Douglas L. Jones","doi":"10.1109/ICASSP.2013.6638796","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638796","url":null,"abstract":"Many applications involve sparse signals with unknown, continuous parameters; a common example is a signal consisting of several sinusoids of unknown frequency. Applying compressed sensing techniques to these signals requires a highly oversampled dictionary for good approximation, but these dictionaries violate the RIP conditions and produce inconsistent results. We consider recovering both a sparse vector and parameter perturbations from an initial set of parameters. Joint recovery allows for accurate reconstructions without highly oversampled dictionaries. Our algorithm for sparse recovery solves a series of linearized subproblems. Recovery error for noiseless simulated measurements is near zero for coarse dictionaries, but increases with the oversampling. This technique is also used to reconstruct Radio Frequency data. The algorithm estimates sharp peaks and transmitter frequencies, demonstrating the potential practical use of the algorithm on real data.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116944593","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}
引用次数: 5
2D orthogonal symmetricwavelet filters using allpass filters 使用全通滤波器的二维正交对称小波滤波器
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638739
Xi Zhang
{"title":"2D orthogonal symmetricwavelet filters using allpass filters","authors":"Xi Zhang","doi":"10.1109/ICASSP.2013.6638739","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638739","url":null,"abstract":"This paper proposes a new class of 2D orthogonal symmetric wavelet filters using 2D nonseparable allpass filters. The proposed wavelet filters are based on the parallel structure of allpass filters with real-valued coefficients, which can be implemented with a low computational complexity and is robust to finite precision effects. The resulting wavelet bases are not only orthogonal, including perfect reconstruction (PR) condition, but also symmetric, whose analysis and synthesis filters have exactly linear phase response. It is also shown that the design problem of the proposed wavelet filters can be reduced to the phase approximation of the corresponding allpass filters. Therefore, it is easy to design this class of orthogonal symmetric wavelet filters by using the existing design methods of allpass filters. Finally, some examples are presented to demonstrate the effectiveness of the proposed orthogonal symmetric wavelet filters.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116304429","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}
引用次数: 1
Joint spatial-temporal filter design for analysis of motor imagery EEG 运动图像脑电分析的联合时空滤波器设计
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6637795
Xinyang Li, Haihong Zhang, Cuntai Guan, S. Ong, Yaozhang Pan, K. Ang
{"title":"Joint spatial-temporal filter design for analysis of motor imagery EEG","authors":"Xinyang Li, Haihong Zhang, Cuntai Guan, S. Ong, Yaozhang Pan, K. Ang","doi":"10.1109/ICASSP.2013.6637795","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6637795","url":null,"abstract":"This paper addresses the key issue of discriminative feature extraction of electroencephalogram (EEG) signals in brain-computer interfaces. Recent advances in neuroscience indicate that multiple brain regions can be activated during motor imagery. The signal propagation among the regions can give rise to spurious effects in identifying event-related desynchronization/synchronization for discriminative motor imagery detection in conventional feature extraction methods. Particularly, we propose that computational models which account for both signal propagation and volume conduction effects of the source neuronal activities can more accurately describe EEG during the specific brain activities and lead to more effective feature extraction. To this end, we devise a unified model for joint learning of signal propagation and spatial patterns. The preliminary results obtained with real-world motor imagery EEG data sets confirm that the new methodology can improve classification accuracy with statistical significance.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124106822","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}
引用次数: 1
A collaborative 20 questions model for target search with human-machine interaction 基于人机交互的20题目标搜索协同模型
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6638921
Theodoros Tsiligkaridis, Brian M. Sadler, A. Hero
{"title":"A collaborative 20 questions model for target search with human-machine interaction","authors":"Theodoros Tsiligkaridis, Brian M. Sadler, A. Hero","doi":"10.1109/ICASSP.2013.6638921","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6638921","url":null,"abstract":"We consider the problem of 20 questions with noise for collaborative players under the minimum entropy criterion [1] in the setting of stochastic search, with application to target localization. First, assuming conditionally independent collaborators, we characterize the structure of the optimal policy for constructing the sequence of questions. This generalizes the single player probabilistic bisection method [1, 2] for stochastic search problems. Second, we prove a separation theorem showing that optimal joint queries achieve the same performance as a greedy sequential scheme. Third, we establish convergence rates of the mean-square error (MSE). Fourth, we derive upper bounds on the MSE of the sequential scheme. This framework provides a mathematical model for incorporating a human in the loop for active machine learning systems.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125981957","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}
引用次数: 6
Where are the challenges in speaker diarization? 说话人化的挑战在哪里?
2013 IEEE International Conference on Acoustics, Speech and Signal Processing Pub Date : 2013-10-21 DOI: 10.1109/ICASSP.2013.6639170
M. Sinclair, Simon King
{"title":"Where are the challenges in speaker diarization?","authors":"M. Sinclair, Simon King","doi":"10.1109/ICASSP.2013.6639170","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6639170","url":null,"abstract":"We present a study on the contributions to Diarization Error Rate by the various components of speaker diarization system. Following on from an earlier study by Huijbregts and Wooters, we extend into more areas and draw somewhat different conclusions. From a series of experiments combining real, oracle and ideal system components, we are able to conclude that the primary cause of error in diarization is the training of speaker models on impure data, something that is in fact done in every current system. We conclude by suggesting ways to improve future systems, including a focus on training the speaker models from smaller quantities of pure data instead of all the data, as is currently done.","PeriodicalId":183968,"journal":{"name":"2013 IEEE International Conference on Acoustics, Speech and Signal Processing","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133677640","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}
引用次数: 26
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