Pub Date : 2019-05-12DOI: 10.1109/ICASSP.2019.8683867
Qing Shen, Wei Liu, Li Wang, Yin Liu
The target localization problem for distributed sensor array networks where a sub-array is placed at each receiver is studied, and under the compressive sensing (CS) framework, a group sparsity based two-dimensional localization method is proposed. Instead of fusing the separately estimated angles of arrival (AOAs), it processes the information collected by all the receivers simultaneously to form the final target locations. Simulation results show that the proposed localization method provides a significant performance improvement compared with the commonly used maximum likelihood estimator (MLE).
{"title":"Group Sparsity Based Target Localization for Distributed Sensor Array Networks","authors":"Qing Shen, Wei Liu, Li Wang, Yin Liu","doi":"10.1109/ICASSP.2019.8683867","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683867","url":null,"abstract":"The target localization problem for distributed sensor array networks where a sub-array is placed at each receiver is studied, and under the compressive sensing (CS) framework, a group sparsity based two-dimensional localization method is proposed. Instead of fusing the separately estimated angles of arrival (AOAs), it processes the information collected by all the receivers simultaneously to form the final target locations. Simulation results show that the proposed localization method provides a significant performance improvement compared with the commonly used maximum likelihood estimator (MLE).","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"101 1","pages":"4190-4194"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77440245","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8683844
Yu Zhu, F. J. Garcia, A. Marques, Santiago Segarra
We study the problem of jointly estimating several network processes that are driven by the same input, recasting it as one of blind identification of a bank of graph filters. More precisely, we consider the observation of several graph signals – i.e., signals defined on the nodes of a graph – and we model each of these signals as the output of a different network process (represented by a graph filter) defined on a common known graph and driven by a common unknown input. Our goal is to recover the specifications of every network process by only observing the outputs. Since every process shares the same input, the estimation problems are coupled, and a joint inference method is proposed. We study two different scenarios, one where the orders of the filters are known, and one where they are not. For the former case we propose a least-squares approach and provide conditions for recovery. For the latter case, we put forth a sparse recovery algorithm with theoretical guarantees. Finally, we illustrate the methods here proposed via numerical experiments.
{"title":"Estimation of Network Processes via Blind Graph Multi-filter Identification","authors":"Yu Zhu, F. J. Garcia, A. Marques, Santiago Segarra","doi":"10.1109/ICASSP.2019.8683844","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683844","url":null,"abstract":"We study the problem of jointly estimating several network processes that are driven by the same input, recasting it as one of blind identification of a bank of graph filters. More precisely, we consider the observation of several graph signals – i.e., signals defined on the nodes of a graph – and we model each of these signals as the output of a different network process (represented by a graph filter) defined on a common known graph and driven by a common unknown input. Our goal is to recover the specifications of every network process by only observing the outputs. Since every process shares the same input, the estimation problems are coupled, and a joint inference method is proposed. We study two different scenarios, one where the orders of the filters are known, and one where they are not. For the former case we propose a least-squares approach and provide conditions for recovery. For the latter case, we put forth a sparse recovery algorithm with theoretical guarantees. Finally, we illustrate the methods here proposed via numerical experiments.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"5451-5455"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88927885","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8683309
Yushu Pan, Yuchen Jiao, Tiejian Li, Yuantao Gu
Hyperspectral images (HSIs) clustering problem is a challenge and valuable task due to its inherent complexity and abundant spectral information. Sparse subspace clustering (SSC) and SSC-based methods are widely used in this problem and demonstrate excellent performance. However, considering that HSIs are usually of high dimension, these methods have expensive computing complexity because of the usage of SSC. To solve this problem, we propose a novel approach called SuperPixel and Angle-based HyperSpectral Image Clustering (SPAHSIC). It first extracts the local spectral and spatial information between pixels by superpixel segmentation, and then applies spectral clustering on the similarity matrix built based on subspace principal angles. We implement experiments on real datasets and get a high accuracy, which indicates the effectiveness of our algorithm.
{"title":"An Efficient Algorithm for Hyperspectral Image Clustering","authors":"Yushu Pan, Yuchen Jiao, Tiejian Li, Yuantao Gu","doi":"10.1109/ICASSP.2019.8683309","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683309","url":null,"abstract":"Hyperspectral images (HSIs) clustering problem is a challenge and valuable task due to its inherent complexity and abundant spectral information. Sparse subspace clustering (SSC) and SSC-based methods are widely used in this problem and demonstrate excellent performance. However, considering that HSIs are usually of high dimension, these methods have expensive computing complexity because of the usage of SSC. To solve this problem, we propose a novel approach called SuperPixel and Angle-based HyperSpectral Image Clustering (SPAHSIC). It first extracts the local spectral and spatial information between pixels by superpixel segmentation, and then applies spectral clustering on the similarity matrix built based on subspace principal angles. We implement experiments on real datasets and get a high accuracy, which indicates the effectiveness of our algorithm.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"140 1","pages":"2167-2171"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77633531","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8683712
D. McGrath, S. Bruhn, H. Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy
Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA). Potentially, the subjective listening experience may be replicated using a compact spatial format with a set number of dynamic objects and scene-based elements, retaining only the perceptual essence of the audio scene. The compact format would further enable a reduction in the complexity of subsequent compression and rendering. This paper investigates these hypotheses by exploring the use of a compact format that consists of up to four dynamic objects and nine HOA channels, with the Enhanced Voice Services (EVS) codec being applied to a 4-channel down-mix of the compact format.
{"title":"Immersive Audio Coding for Virtual Reality Using a Metadata-assisted Extension of the 3GPP EVS Codec","authors":"D. McGrath, S. Bruhn, H. Purnhagen, Michael Eckert, Juan Torres, Stefanie Brown, Dan Darcy","doi":"10.1109/ICASSP.2019.8683712","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683712","url":null,"abstract":"Virtual Reality (VR) audio scenes may be composed of a very large number of audio elements, including dynamic audio objects, fixed audio channels and scene-based audio elements such as Higher Order Ambisonics (HOA). Potentially, the subjective listening experience may be replicated using a compact spatial format with a set number of dynamic objects and scene-based elements, retaining only the perceptual essence of the audio scene. The compact format would further enable a reduction in the complexity of subsequent compression and rendering. This paper investigates these hypotheses by exploring the use of a compact format that consists of up to four dynamic objects and nine HOA channels, with the Enhanced Voice Services (EVS) codec being applied to a 4-channel down-mix of the compact format.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"41 1","pages":"730-734"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77653511","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8682962
Vien V. Mai, M. Johansson
This paper introduces a novel technique for nonlinear acceleration of first-order methods for constrained convex optimization. Previous studies of nonlinear acceleration have only been able to provide convergence guarantees for unconstrained convex optimization. In contrast, our method is able to avoid infeasibility of the accelerated iterates and retains the theoretical performance guarantees of the unconstrained case. We focus on Anderson acceleration of the classical projected gradient descent (PGD) method, but our techniques can easily be extended to more sophisticated algorithms, such as mirror descent. Due to the presence of a constraint set, the relevant fixed-point mapping for PGD is not differentiable. However, we show that the convergence results for Anderson acceleration of smooth fixed-point iterations can be extended to the non-smooth case under certain technical conditions.
{"title":"Nonlinear Acceleration of Constrained Optimization Algorithms","authors":"Vien V. Mai, M. Johansson","doi":"10.1109/ICASSP.2019.8682962","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682962","url":null,"abstract":"This paper introduces a novel technique for nonlinear acceleration of first-order methods for constrained convex optimization. Previous studies of nonlinear acceleration have only been able to provide convergence guarantees for unconstrained convex optimization. In contrast, our method is able to avoid infeasibility of the accelerated iterates and retains the theoretical performance guarantees of the unconstrained case. We focus on Anderson acceleration of the classical projected gradient descent (PGD) method, but our techniques can easily be extended to more sophisticated algorithms, such as mirror descent. Due to the presence of a constraint set, the relevant fixed-point mapping for PGD is not differentiable. However, we show that the convergence results for Anderson acceleration of smooth fixed-point iterations can be extended to the non-smooth case under certain technical conditions.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"17 1","pages":"4903-4907"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78029089","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8682465
Xiao Fu, Cheng Gao, Hoi-To Wai, Kejun Huang
This work focuses on canonical polyadic decomposition (CPD) for large-scale tensors. Many prior works rely on data sparsity to develop scalable CPD algorithms, which are not suitable for handling dense tensor, while dense tensors often arise in applications such as image and video processing. As an alternative, stochastic algorithms utilize data sampling to reduce per-iteration complexity and thus are very scalable, even when handling dense tensors. However, existing stochastic CPD algorithms are facing some challenges. For example, some algorithms are based on randomly sampled tensor entries, and thus each iteration can only updates a small portion of the latent factors. This may result in slow improvement of the estimation accuracy of the latent factors. In addition, the convergence properties of many stochastic CPD algorithms are unclear, perhaps because CPD poses a hard nonconvex problem and is challenging for analysis under stochastic settings. In this work, we propose a stochastic optimization strategy that can effectively circumvent the above challenges. The proposed algorithm updates a whole latent factor at each iteration using sampled fibers of a tensor, which can quickly increase the estimation accuracy. The algorithm is flexible—many commonly used regularizers and constraints can be easily incorporated in the computational framework. The algorithm is also backed by a rigorous convergence theory. Simulations on large-scale dense tensors are employed to showcase the effectiveness of the algorithm.
{"title":"Block-randomized Stochastic Proximal Gradient for Constrained Low-rank Tensor Factorization","authors":"Xiao Fu, Cheng Gao, Hoi-To Wai, Kejun Huang","doi":"10.1109/ICASSP.2019.8682465","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682465","url":null,"abstract":"This work focuses on canonical polyadic decomposition (CPD) for large-scale tensors. Many prior works rely on data sparsity to develop scalable CPD algorithms, which are not suitable for handling dense tensor, while dense tensors often arise in applications such as image and video processing. As an alternative, stochastic algorithms utilize data sampling to reduce per-iteration complexity and thus are very scalable, even when handling dense tensors. However, existing stochastic CPD algorithms are facing some challenges. For example, some algorithms are based on randomly sampled tensor entries, and thus each iteration can only updates a small portion of the latent factors. This may result in slow improvement of the estimation accuracy of the latent factors. In addition, the convergence properties of many stochastic CPD algorithms are unclear, perhaps because CPD poses a hard nonconvex problem and is challenging for analysis under stochastic settings. In this work, we propose a stochastic optimization strategy that can effectively circumvent the above challenges. The proposed algorithm updates a whole latent factor at each iteration using sampled fibers of a tensor, which can quickly increase the estimation accuracy. The algorithm is flexible—many commonly used regularizers and constraints can be easily incorporated in the computational framework. The algorithm is also backed by a rigorous convergence theory. Simulations on large-scale dense tensors are employed to showcase the effectiveness of the algorithm.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"1 1","pages":"7485-7489"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80416538","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8683849
Ilker Gurcan, H. Nguyen
Recently, surgical activity recognition has been receiving significant attention from the medical imaging community. Existing state-of-the-art approaches employ recurrent neural networks such as long-short term memory networks (LSTMs). However, our experiments show that these networks are not effective in capturing the relationship of features with different temporal scales. Such limitation will lead to sub-optimal recognition performance of surgical activities containing complex motions at multiple time scales. To overcome this shortcoming, our paper proposes a multi-scale recurrent neural network (MS-RNN) that combines the strength of both wavelet scattering operations and LSTM. We validate the effectiveness of the proposed network using both real and synthetic datasets. Our experimental results show that MS-RNN outperforms state-of-the-art methods in surgical activity recognition by a significant margin. On a synthetic dataset, the proposed network achieves more than 90% classification accuracy while LSTM’s accuracy is around chance level. Experiments on real surgical activity dataset shows a significant improvement of recognition accuracy over the current state of the art (90.2% versus 83.3%).
{"title":"Surgical Activities Recognition Using Multi-scale Recurrent Networks","authors":"Ilker Gurcan, H. Nguyen","doi":"10.1109/ICASSP.2019.8683849","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8683849","url":null,"abstract":"Recently, surgical activity recognition has been receiving significant attention from the medical imaging community. Existing state-of-the-art approaches employ recurrent neural networks such as long-short term memory networks (LSTMs). However, our experiments show that these networks are not effective in capturing the relationship of features with different temporal scales. Such limitation will lead to sub-optimal recognition performance of surgical activities containing complex motions at multiple time scales. To overcome this shortcoming, our paper proposes a multi-scale recurrent neural network (MS-RNN) that combines the strength of both wavelet scattering operations and LSTM. We validate the effectiveness of the proposed network using both real and synthetic datasets. Our experimental results show that MS-RNN outperforms state-of-the-art methods in surgical activity recognition by a significant margin. On a synthetic dataset, the proposed network achieves more than 90% classification accuracy while LSTM’s accuracy is around chance level. Experiments on real surgical activity dataset shows a significant improvement of recognition accuracy over the current state of the art (90.2% versus 83.3%).","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"13 1","pages":"2887-2891"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82478817","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8682424
Zheng Yu, Wenmin Wang, Ge Li
Cross-modal retrieval has been recently proposed to find an appropriate subspace where the similarity among different modalities, such as image and text, can be directly measured. In this paper, we propose Multi-step Self-Attention Network (MSAN) to perform cross-modal retrieval in a limited text space with multiple attention steps, that can selectively attend to partial shared information at each step and aggregate useful information over multiple steps to measure the final similarity. In order to achieve better retrieval results with faster training speed, we introduce global prior knowledge as the global reference information. Extensive experiments on Flickr30K and MSCOCO, show that MSAN achieves new state-of-the-art results in accuracy for cross-modal retrieval.
{"title":"Multi-step Self-attention Network for Cross-modal Retrieval Based on a Limited Text Space","authors":"Zheng Yu, Wenmin Wang, Ge Li","doi":"10.1109/ICASSP.2019.8682424","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682424","url":null,"abstract":"Cross-modal retrieval has been recently proposed to find an appropriate subspace where the similarity among different modalities, such as image and text, can be directly measured. In this paper, we propose Multi-step Self-Attention Network (MSAN) to perform cross-modal retrieval in a limited text space with multiple attention steps, that can selectively attend to partial shared information at each step and aggregate useful information over multiple steps to measure the final similarity. In order to achieve better retrieval results with faster training speed, we introduce global prior knowledge as the global reference information. Extensive experiments on Flickr30K and MSCOCO, show that MSAN achieves new state-of-the-art results in accuracy for cross-modal retrieval.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"16 1","pages":"2082-2086"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82559134","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8682412
R. Nagar, S. Raman
Reflection symmetry is ubiquitous in nature and plays an important role in object detection and recognition tasks. Most of the existing methods for symmetry detection extract and describe each keypoint using a descriptor and a mirrored descriptor. Two keypoints are said to be mirror symmetric key-points if the original descriptor of one keypoint and the mirrored descriptor of the other keypoint are similar. However, these methods suffer from the following issue. The background pixels around the mirror symmetric pixels lying on the boundary of an object can be different. Therefore, their descriptors can be different. However, the boundary of a symmetric object is a major component of global reflection symmetry. We exploit the estimated boundary of the object and describe a boundary pixel using only the estimated normal of the boundary segment around the pixel. We embed the symmetry axes in a graph as cliques to robustly detect the symmetry axes. We show that this approach achieves state-of-the-art results in a standard dataset.
{"title":"Reflection Symmetry Detection by Embedding Symmetry in a Graph","authors":"R. Nagar, S. Raman","doi":"10.1109/ICASSP.2019.8682412","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682412","url":null,"abstract":"Reflection symmetry is ubiquitous in nature and plays an important role in object detection and recognition tasks. Most of the existing methods for symmetry detection extract and describe each keypoint using a descriptor and a mirrored descriptor. Two keypoints are said to be mirror symmetric key-points if the original descriptor of one keypoint and the mirrored descriptor of the other keypoint are similar. However, these methods suffer from the following issue. The background pixels around the mirror symmetric pixels lying on the boundary of an object can be different. Therefore, their descriptors can be different. However, the boundary of a symmetric object is a major component of global reflection symmetry. We exploit the estimated boundary of the object and describe a boundary pixel using only the estimated normal of the boundary segment around the pixel. We embed the symmetry axes in a graph as cliques to robustly detect the symmetry axes. We show that this approach achieves state-of-the-art results in a standard dataset.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"4012 2 1","pages":"2147-2151"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86699508","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 : 2019-05-12DOI: 10.1109/ICASSP.2019.8682305
T. Ferreira, W. Martins, Markus V. S. Lima, P. Diniz
Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might sometimes lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one.
{"title":"Convex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms","authors":"T. Ferreira, W. Martins, Markus V. S. Lima, P. Diniz","doi":"10.1109/ICASSP.2019.8682305","DOIUrl":"https://doi.org/10.1109/ICASSP.2019.8682305","url":null,"abstract":"Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might sometimes lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one.","PeriodicalId":13203,"journal":{"name":"ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"222 1","pages":"4858-4862"},"PeriodicalIF":0.0,"publicationDate":"2019-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89124154","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：