Pub Date : 2013-05-26DOI: 10.1109/ICASSP.2013.6637950
T. Fonseca, R. Queiroz
Energy consumption has become a leading design constraint for computing devices in order to defray electric bills for individuals and businesses. Over the past years, digital video communication technologies have demanded higher computing power availability and, therefore, higher energy expenditure. In order to meet the challenge to provide software-based video encoding solutions, we adopted an open source software implementation of an H.264 video encoder, the x264 encoder, and optimized its prediction stage in the energy sense (E). Thus, besides looking for the coding options which lead to the best coded representation in terms of rate and distortion (RD), we constrain the process to fit within a certain energy budget. i.e., an RDE optimization. We considered energy as the time integration of the real demanded electric power for a given system. We present an RDE-optimized framework which allows for software-based real-time video compression, meeting the desired targets of electrical consumption, hence, controlling carbon emissions. We show results of energy-constrained compression wherein one can save as much as 35% of the energy with small impact on RD performance.
{"title":"Energy-constrained real-time H.264/AVC video coding","authors":"T. Fonseca, R. Queiroz","doi":"10.1109/ICASSP.2013.6637950","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6637950","url":null,"abstract":"Energy consumption has become a leading design constraint for computing devices in order to defray electric bills for individuals and businesses. Over the past years, digital video communication technologies have demanded higher computing power availability and, therefore, higher energy expenditure. In order to meet the challenge to provide software-based video encoding solutions, we adopted an open source software implementation of an H.264 video encoder, the x264 encoder, and optimized its prediction stage in the energy sense (E). Thus, besides looking for the coding options which lead to the best coded representation in terms of rate and distortion (RD), we constrain the process to fit within a certain energy budget. i.e., an RDE optimization. We considered energy as the time integration of the real demanded electric power for a given system. We present an RDE-optimized framework which allows for software-based real-time video compression, meeting the desired targets of electrical consumption, hence, controlling carbon emissions. We show results of energy-constrained compression wherein one can save as much as 35% of the energy with small impact on RD performance.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88712903","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}
Pub Date : 2013-05-26DOI: 10.1109/ICASSP.2013.6639080
H. V. hamme
Signal models where non-negative vector data are represented by a sparse linear combination of non-negative basis vectors have attracted much attention in problems including image classification, document topic modeling, sound source segregation and robust speech recognition. In this paper, an iterative algorithm based on Newton updates to minimize the Kullback-Leibler divergence between data and model is proposed. It finds the sparse activation weights of the basis vectors more efficiently than the expectation-maximization (EM) algorithm. To avoid the computational burden of a matrix inversion, a diagonal approximation is made and therefore the algorithm is called diagonal Newton Algorithm (DNA). It is several times faster than EM, especially for undercomplete problems. But DNA also performs surprisingly well on overcomplete problems.
{"title":"A diagonalized newton algorithm for non-negative sparse coding","authors":"H. V. hamme","doi":"10.1109/ICASSP.2013.6639080","DOIUrl":"https://doi.org/10.1109/ICASSP.2013.6639080","url":null,"abstract":"Signal models where non-negative vector data are represented by a sparse linear combination of non-negative basis vectors have attracted much attention in problems including image classification, document topic modeling, sound source segregation and robust speech recognition. In this paper, an iterative algorithm based on Newton updates to minimize the Kullback-Leibler divergence between data and model is proposed. It finds the sparse activation weights of the basis vectors more efficiently than the expectation-maximization (EM) algorithm. To avoid the computational burden of a matrix inversion, a diagonal approximation is made and therefore the algorithm is called diagonal Newton Algorithm (DNA). It is several times faster than EM, especially for undercomplete problems. But DNA also performs surprisingly well on overcomplete problems.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2013-05-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73828322","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6288143
M. Iwahashi, H. Kobayashi, H. Kiya
In this paper, a lossy data compression for a sparse histogram image signal is proposed. It is extended from an existing lossless coding which is based on a lossless histogram packing and a lossless coding. We introduce a lossy mapping, which has less computational load than the rate-distortion optimized Lloyd-Max quantization, and combine it with a lossless coding. It was confirmed that the proposed method attains higher performance in the rate-distortion plane than existing methods. This is because it can utilize histogram sparseness of images, and also its inverse mapping does not magnify quantization noise.
{"title":"Lossy compression of sparse histogram image","authors":"M. Iwahashi, H. Kobayashi, H. Kiya","doi":"10.1109/ICASSP.2012.6288143","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288143","url":null,"abstract":"In this paper, a lossy data compression for a sparse histogram image signal is proposed. It is extended from an existing lossless coding which is based on a lossless histogram packing and a lossless coding. We introduce a lossy mapping, which has less computational load than the rate-distortion optimized Lloyd-Max quantization, and combine it with a lossless coding. It was confirmed that the proposed method attains higher performance in the rate-distortion plane than existing methods. This is because it can utilize histogram sparseness of images, and also its inverse mapping does not magnify quantization noise.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77355736","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6288924
Y. Shiga
This paper investigates how the quality of speech produced through statistical parametric synthesis is affected by anti-aliasing filtering, i.e., low-pass filtering that is applied prior to (down-) sampling prerecorded speech at a desired rate. It has empirically been known that the frequency response of such anti-aliasing filters influences the quality of speech synthesized to a considerable degree. For the purpose of understanding such influence more clearly, in this paper we examine the spectral aspects of speech involved in the processes of HMM training and synthesis. We then propose a technique of feature extraction that can avoid producing the roll-off feature of the frequency response near the Nyquist frequency, which is found to be the major cause of speech quality degradation resulting from anti-aliasing filtering. In the technique, the spectrum is first computed from speech at a sampling rate higher than the desired rate, then it is truncated so that its frequency range above the target Nyquist frequency is discarded, and finally the truncated spectrum is converted directly into the cepstrum. Listening test results show that the proposed technique enables training HMMs efficiently with a limited number of model parameters and effectively with less artifacts in the speech synthesized at a desired sampling rate.
{"title":"Effect of anti-aliasing filtering on the quality of speech from an HMM-based synthesizer","authors":"Y. Shiga","doi":"10.1109/ICASSP.2012.6288924","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288924","url":null,"abstract":"This paper investigates how the quality of speech produced through statistical parametric synthesis is affected by anti-aliasing filtering, i.e., low-pass filtering that is applied prior to (down-) sampling prerecorded speech at a desired rate. It has empirically been known that the frequency response of such anti-aliasing filters influences the quality of speech synthesized to a considerable degree. For the purpose of understanding such influence more clearly, in this paper we examine the spectral aspects of speech involved in the processes of HMM training and synthesis. We then propose a technique of feature extraction that can avoid producing the roll-off feature of the frequency response near the Nyquist frequency, which is found to be the major cause of speech quality degradation resulting from anti-aliasing filtering. In the technique, the spectrum is first computed from speech at a sampling rate higher than the desired rate, then it is truncated so that its frequency range above the target Nyquist frequency is discarded, and finally the truncated spectrum is converted directly into the cepstrum. Listening test results show that the proposed technique enables training HMMs efficiently with a limited number of model parameters and effectively with less artifacts in the speech synthesized at a desired sampling rate.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83488176","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6288438
Bo Li, Y. Zou
The performance of DOA estimation with scalar sensor arrays using spatial sparse signal reconstruction (SSR) technique is affected by the grid spacing. In this paper, we formulate the DOA estimation with the acoustic vector sensor (AVS) arrays under SSR framework. A coarse-to-fine DOA estimation algorithm has been developed. The source spatial sparsity and the inter-relations among the manifold matrices of the AVS subarrays are jointly utilized to eliminate the grid effect in the SSR technique and the improvement of the overall DOA estimation performance is achieved at low complexity. Simulation results show that the proposed method effectively mitigates the DOA estimation bias caused by off-grid sources. Interestingly, our method gives good DOA estimation accuracy when sources are closely located.
{"title":"Improved DOA estimation with acoustic vector sensor arrays using spatial sparsity and subarray manifold","authors":"Bo Li, Y. Zou","doi":"10.1109/ICASSP.2012.6288438","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288438","url":null,"abstract":"The performance of DOA estimation with scalar sensor arrays using spatial sparse signal reconstruction (SSR) technique is affected by the grid spacing. In this paper, we formulate the DOA estimation with the acoustic vector sensor (AVS) arrays under SSR framework. A coarse-to-fine DOA estimation algorithm has been developed. The source spatial sparsity and the inter-relations among the manifold matrices of the AVS subarrays are jointly utilized to eliminate the grid effect in the SSR technique and the improvement of the overall DOA estimation performance is achieved at low complexity. Simulation results show that the proposed method effectively mitigates the DOA estimation bias caused by off-grid sources. Interestingly, our method gives good DOA estimation accuracy when sources are closely located.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78711463","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6288134
Y. Wen, Lianghua He, Yue Lu
The discriminative common vectors (DCV) algorithm shows better face recognition effects than some commonly used linear discriminant algorithms, which uses the subspace methods and the Gram-Schmidt orthogonalization (GSO) procedure to obtain the DCV. However, the Gram-Schmidt technique may produce a set of vectors which is far from orthogonal so that sometimes the orthogonality may be lost completely. Hence, the effectiveness of the DCV is also decreased. In this paper, we proposed an improved DCV method based on the GSO. For obtaining an accurate projection onto the corresponding space, the orthogonal basis problem is usually solved with the Gram-Schmidt process with reorthogonalization. Thus, the effectiveness of the DCV can be improved and the experimental results show that the proposed method is better for the small sample size problem as compared to the DCV.
判别公向量(discriminative common vector, DCV)算法采用子空间方法和Gram-Schmidt正交化(GSO)方法得到的判别公向量(discriminative common vector, DCV)算法，其人脸识别效果优于一些常用的线性判别算法。然而，Gram-Schmidt技术可能产生一组远离正交的向量，以至于有时会完全失去正交性。因此，DCV的有效性也降低了。在本文中，我们提出了一种基于GSO的改进DCV方法。为了得到相应空间上的精确投影，正交基问题通常采用重新正交化的Gram-Schmidt过程来解决。实验结果表明，该方法比DCV方法更适合小样本量问题。
{"title":"Discriminative common vectors based on the Gram-Schmidt reorthogonalization for the small sample size problem","authors":"Y. Wen, Lianghua He, Yue Lu","doi":"10.1109/ICASSP.2012.6288134","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288134","url":null,"abstract":"The discriminative common vectors (DCV) algorithm shows better face recognition effects than some commonly used linear discriminant algorithms, which uses the subspace methods and the Gram-Schmidt orthogonalization (GSO) procedure to obtain the DCV. However, the Gram-Schmidt technique may produce a set of vectors which is far from orthogonal so that sometimes the orthogonality may be lost completely. Hence, the effectiveness of the DCV is also decreased. In this paper, we proposed an improved DCV method based on the GSO. For obtaining an accurate projection onto the corresponding space, the orthogonal basis problem is usually solved with the Gram-Schmidt process with reorthogonalization. Thus, the effectiveness of the DCV can be improved and the experimental results show that the proposed method is better for the small sample size problem as compared to the DCV.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75775876","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6288294
B. S. Jensen, J. S. Gallego, Jan Larsen
Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to model - and in turn predict - the pairwise music preference using a very flexible model based on Gaussian Process priors for which we describe the required inference. We further propose a specific covariance function and evaluate the predictive performance on a novel dataset. In a recommendation style setting we obtain a leave-one-out accuracy of 74% compared to 50% with random predictions, showing potential for further refinement and evaluation.
{"title":"A predictive model of music preference using pairwise comparisons","authors":"B. S. Jensen, J. S. Gallego, Jan Larsen","doi":"10.1109/ICASSP.2012.6288294","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288294","url":null,"abstract":"Music recommendation is an important aspect of many streaming services and multi-media systems, however, it is typically based on so-called collaborative filtering methods. In this paper we consider the recommendation task from a personal viewpoint and examine to which degree music preference can be elicited and predicted using simple and robust queries such as pairwise comparisons. We propose to model - and in turn predict - the pairwise music preference using a very flexible model based on Gaussian Process priors for which we describe the required inference. We further propose a specific covariance function and evaluate the predictive performance on a novel dataset. In a recommendation style setting we obtain a leave-one-out accuracy of 74% compared to 50% with random predictions, showing potential for further refinement and evaluation.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76151496","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6289063
Yangyang Shi, P. Wiggers, C. Jonker
We propose a dynamic Bayesian classifier for the socio-situational setting of a conversation. Knowledge of the socio-situational setting can be used to search for content recorded in a particular setting or to select context-dependent models in speech recognition. The dynamic Bayesian classifier has the advantage - compared to static classifiers such a naive Bayes and support vector machines - that it can continuously update the classification during a conversation. We experimented with several models that use lexical and part-of-speech information. Our results show that the prediction accuracy of the dynamic Bayesian classifier using the first 25% of a conversation is almost 98% of the final prediction accuracy, which is calculated on the entire conversation. The best final prediction accuracy, 88.85%, is obtained by bigram dynamic Bayesian classification using words and part-of-speech tags.
{"title":"Dynamic Bayesian socio-situational setting classification","authors":"Yangyang Shi, P. Wiggers, C. Jonker","doi":"10.1109/ICASSP.2012.6289063","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6289063","url":null,"abstract":"We propose a dynamic Bayesian classifier for the socio-situational setting of a conversation. Knowledge of the socio-situational setting can be used to search for content recorded in a particular setting or to select context-dependent models in speech recognition. The dynamic Bayesian classifier has the advantage - compared to static classifiers such a naive Bayes and support vector machines - that it can continuously update the classification during a conversation. We experimented with several models that use lexical and part-of-speech information. Our results show that the prediction accuracy of the dynamic Bayesian classifier using the first 25% of a conversation is almost 98% of the final prediction accuracy, which is calculated on the entire conversation. The best final prediction accuracy, 88.85%, is obtained by bigram dynamic Bayesian classification using words and part-of-speech tags.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84361948","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6288483
Zai Yang, Cisheng Zhang, Lihua Xie
The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is exactly known. The CS problem subject to perturbation in the sensing matrix is often encountered in practice and has attracted interest of researches. Unlike existing robust signal recoveries with the recovery error growing linearly with the perturbation level, this paper analyzes the CS problem subject to a structured perturbation to provide conditions for stable signal recovery under measurement noise. Under mild conditions on the perturbed sensing matrix, similar to that for the standard CS, it is shown that a sparse signal can be stably recovered by ℓ1 minimization. A remarkable result is that the recovery is exact and independent of the perturbation if there is no measurement noise and the signal is sufficiently sparse. In the presence of noise, largest entries (in magnitude) of a compressible signal can be stably recovered. The result is demonstrated by a simulation example.
{"title":"Stable signal recovery in compressed sensing with a structured matrix perturbation","authors":"Zai Yang, Cisheng Zhang, Lihua Xie","doi":"10.1109/ICASSP.2012.6288483","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6288483","url":null,"abstract":"The sparse signal recovery in standard compressed sensing (CS) requires that the sensing matrix is exactly known. The CS problem subject to perturbation in the sensing matrix is often encountered in practice and has attracted interest of researches. Unlike existing robust signal recoveries with the recovery error growing linearly with the perturbation level, this paper analyzes the CS problem subject to a structured perturbation to provide conditions for stable signal recovery under measurement noise. Under mild conditions on the perturbed sensing matrix, similar to that for the standard CS, it is shown that a sparse signal can be stably recovered by ℓ1 minimization. A remarkable result is that the recovery is exact and independent of the perturbation if there is no measurement noise and the signal is sufficiently sparse. In the presence of noise, largest entries (in magnitude) of a compressible signal can be stably recovered. The result is demonstrated by a simulation example.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88500882","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}
Pub Date : 2012-08-31DOI: 10.1109/ICASSP.2012.6289111
S. Nitinawarat, George K. Atia, V. Veeravalli
In this paper, the problem of multiple hypothesis testing with observation control is considered. The structure of the optimal controller under various asymptotic regimes is studied. First, a setup with a fixed sample size is considered. In this setup, the asymptotic quantity of interest is the optimal exponent for the maximal error probability. For the case of binary hypothesis testing, it is shown that the optimal error exponent corresponds to the maximum Chernoff information over the choice of controls. It is also shown that a pure stationary control policy, i.e., a fixed policy which does not depend on specific realizations of past measurements and past controls (open-loop), is asymptotically optimal even among the class of all causal control policies. We also derive lower and upper bounds for the optimal error exponent for the case of multiple hypothesis testing. Second, a sequential setup is considered wherein the controller can also decide when to stop taking observations. In this case, the objective is to minimize the expected stopping time subject to the constraints of vanishing error probabilities under each hypothesis. A sequential test is proposed for testing multiple hypotheses and is shown to be asymptotically optimal.
{"title":"Controlled sensing for hypothesis testing","authors":"S. Nitinawarat, George K. Atia, V. Veeravalli","doi":"10.1109/ICASSP.2012.6289111","DOIUrl":"https://doi.org/10.1109/ICASSP.2012.6289111","url":null,"abstract":"In this paper, the problem of multiple hypothesis testing with observation control is considered. The structure of the optimal controller under various asymptotic regimes is studied. First, a setup with a fixed sample size is considered. In this setup, the asymptotic quantity of interest is the optimal exponent for the maximal error probability. For the case of binary hypothesis testing, it is shown that the optimal error exponent corresponds to the maximum Chernoff information over the choice of controls. It is also shown that a pure stationary control policy, i.e., a fixed policy which does not depend on specific realizations of past measurements and past controls (open-loop), is asymptotically optimal even among the class of all causal control policies. We also derive lower and upper bounds for the optimal error exponent for the case of multiple hypothesis testing. Second, a sequential setup is considered wherein the controller can also decide when to stop taking observations. In this case, the objective is to minimize the expected stopping time subject to the constraints of vanishing error probabilities under each hypothesis. A sequential test is proposed for testing multiple hypotheses and is shown to be asymptotically optimal.","PeriodicalId":6443,"journal":{"name":"2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2012-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80563188","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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