Pub Date : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9054515
Van Ha Tang, A. Bouzerdoum, S. L. Phung
This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the observed radar signals, a joint distribution of the observed data and latent variables is formulated by incorporating two important beliefs: low-dimensional structure of wall reflections and joint sparsity among channel images. These priors are modeled through probabilistic distributions whose hyperparameters are treated with a full Bayesian formulation. Furthermore, the paper presents a variational Bayesian inference algorithm that captures wall clutter and provides channel images as full posterior distributions. Experimental results on real data show that the proposed model is very effective at removing wall clutter and enhancing target localization.
{"title":"A Variational Bayesian Approach for Multichannel Through-Wall Radar Imaging with Low-Rank and Sparse Priors","authors":"Van Ha Tang, A. Bouzerdoum, S. L. Phung","doi":"10.1109/ICASSP40776.2020.9054515","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054515","url":null,"abstract":"This paper considers the problem of multichannel through-wall radar (TWR) imaging from a probabilistic Bayesian perspective. Given the observed radar signals, a joint distribution of the observed data and latent variables is formulated by incorporating two important beliefs: low-dimensional structure of wall reflections and joint sparsity among channel images. These priors are modeled through probabilistic distributions whose hyperparameters are treated with a full Bayesian formulation. Furthermore, the paper presents a variational Bayesian inference algorithm that captures wall clutter and provides channel images as full posterior distributions. Experimental results on real data show that the proposed model is very effective at removing wall clutter and enhancing target localization.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"961 1","pages":"2523-2527"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85623736","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9054731
M. Terris, A. Repetti, J. Pesquet, Y. Wiaux
Building nonexpansive Convolutional Neural Networks (CNNs) is a challenging problem that has recently gained a lot of attention from the image processing community. In particular, it appears to be the key to obtain convergent Plugand-Play algorithms. This problem, which relies on an accurate control of the the Lipschitz constant of the convolutional layers, has also been investigated for Generative Adversarial Networks to improve robustness to adversarial perturbations. However, to the best of our knowledge, no efficient method has been developed yet to build nonexpansive CNNs. In this paper, we develop an optimization algorithm that can be incorporated in the training of a network to ensure the nonexpansiveness of its convolutional layers. This is shown to allow us to build firmly nonexpansive CNNs. We apply the proposed approach to train a CNN for an image denoising task and show its effectiveness through simulations.
{"title":"Building Firmly Nonexpansive Convolutional Neural Networks","authors":"M. Terris, A. Repetti, J. Pesquet, Y. Wiaux","doi":"10.1109/ICASSP40776.2020.9054731","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054731","url":null,"abstract":"Building nonexpansive Convolutional Neural Networks (CNNs) is a challenging problem that has recently gained a lot of attention from the image processing community. In particular, it appears to be the key to obtain convergent Plugand-Play algorithms. This problem, which relies on an accurate control of the the Lipschitz constant of the convolutional layers, has also been investigated for Generative Adversarial Networks to improve robustness to adversarial perturbations. However, to the best of our knowledge, no efficient method has been developed yet to build nonexpansive CNNs. In this paper, we develop an optimization algorithm that can be incorporated in the training of a network to ensure the nonexpansiveness of its convolutional layers. This is shown to allow us to build firmly nonexpansive CNNs. We apply the proposed approach to train a CNN for an image denoising task and show its effectiveness through simulations.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"74 1","pages":"8658-8662"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85992469","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9053086
Rohan Kumar Das, Jichen Yang, Haizhou Li
Most of the research on anti-spoofing countermeasures are specific to a type of spoofing attacks, where models are trained on data of a particular nature, either synthetic or replay. However, one does not have such leverage as there is no prior knowledge about the kind of spoofing attack in practice. Therefore, there is a requirement to assess the scope of generalized countermeasures for anti-spoofing. The ASVspoof 2019challengecoversboth synthetic as well as replay attacks, which makes the database suitable for such study. In this work, we consider widely popular constant-Q cepstral coefficient features along with two other promising front-ends that capture long-term signal characteristics to assess their scope as generalized countermeasures. Additionally, a comprehensive study is made across different editions of ASVspoof corpora to highlight the need of robust generalized countermeasures in unseen conditions.
{"title":"Assessing the Scope of Generalized Countermeasures for Anti-Spoofing","authors":"Rohan Kumar Das, Jichen Yang, Haizhou Li","doi":"10.1109/ICASSP40776.2020.9053086","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053086","url":null,"abstract":"Most of the research on anti-spoofing countermeasures are specific to a type of spoofing attacks, where models are trained on data of a particular nature, either synthetic or replay. However, one does not have such leverage as there is no prior knowledge about the kind of spoofing attack in practice. Therefore, there is a requirement to assess the scope of generalized countermeasures for anti-spoofing. The ASVspoof 2019challengecoversboth synthetic as well as replay attacks, which makes the database suitable for such study. In this work, we consider widely popular constant-Q cepstral coefficient features along with two other promising front-ends that capture long-term signal characteristics to assess their scope as generalized countermeasures. Additionally, a comprehensive study is made across different editions of ASVspoof corpora to highlight the need of robust generalized countermeasures in unseen conditions.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"312 1","pages":"6589-6593"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76891659","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9054141
Xudong Zhao, Gongping Huang, Jingdong Chen, J. Benesty
Frequency-invariant beamforming with circular microphone arrays (CMAs) has drawn a significant amount of attention for its steering flexibility and high directivity. However, frequency-invariant beam-forming with CMAs often suffers from the so-called null problem, which is caused by the zeros of the Bessel functions; then, concentric CMAs (CCMAs) are used to deal with this problem. While frequency-invariant beamforming with CCMAs can mitigate the null problem, the beampattern is still suffering from distortion due to s-patial aliasing at high frequencies. In this paper, we find that the spatial aliasing problem is caused by higher-order circular harmonics. To deal with this problem, we take the aliasing harmonics into account and approximate the beampattern with a higher truncation order of the Jacobi-Anger expansion than required. Then, the beam-forming filter is determined by minimizing the errors between the desired directivity pattern and the approximated one. Simulation results show that the developed method can mitigate the distortion of the beampattern caused by spatial aliasing.
{"title":"An Improved Solution to the Frequency-Invariant Beamforming with Concentric Circular Microphone Arrays","authors":"Xudong Zhao, Gongping Huang, Jingdong Chen, J. Benesty","doi":"10.1109/ICASSP40776.2020.9054141","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054141","url":null,"abstract":"Frequency-invariant beamforming with circular microphone arrays (CMAs) has drawn a significant amount of attention for its steering flexibility and high directivity. However, frequency-invariant beam-forming with CMAs often suffers from the so-called null problem, which is caused by the zeros of the Bessel functions; then, concentric CMAs (CCMAs) are used to deal with this problem. While frequency-invariant beamforming with CCMAs can mitigate the null problem, the beampattern is still suffering from distortion due to s-patial aliasing at high frequencies. In this paper, we find that the spatial aliasing problem is caused by higher-order circular harmonics. To deal with this problem, we take the aliasing harmonics into account and approximate the beampattern with a higher truncation order of the Jacobi-Anger expansion than required. Then, the beam-forming filter is determined by minimizing the errors between the desired directivity pattern and the approximated one. Simulation results show that the developed method can mitigate the distortion of the beampattern caused by spatial aliasing.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"5 1","pages":"556-560"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76979391","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9054231
Nikhar P. Rakhashia, Ankit A. Bhurane, V. Gadre
Signals and systems is a well known fundamental course in signal processing. How this course is taught to a student can spell the difference between whether s/he pursues a career in this field or not. Giving due consideration to this matter, this paper reflects on the experiences in teaching this course. In addition, the authors share the experiences of creating and conducting a Massive Open Online Course (MOOC) on this subject under edX and subsequently following it up with deliberation among some students who did this course through the platform. Further, this paper emphasizes on various active learning techniques and modes of evaluation to ensure effective and holistic learning of the course.
{"title":"Teaching Signals and Systems - A First Course in Signal Processing","authors":"Nikhar P. Rakhashia, Ankit A. Bhurane, V. Gadre","doi":"10.1109/ICASSP40776.2020.9054231","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054231","url":null,"abstract":"Signals and systems is a well known fundamental course in signal processing. How this course is taught to a student can spell the difference between whether s/he pursues a career in this field or not. Giving due consideration to this matter, this paper reflects on the experiences in teaching this course. In addition, the authors share the experiences of creating and conducting a Massive Open Online Course (MOOC) on this subject under edX and subsequently following it up with deliberation among some students who did this course through the platform. Further, this paper emphasizes on various active learning techniques and modes of evaluation to ensure effective and holistic learning of the course.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"247 1","pages":"9224-9228"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76987245","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9053757
S. Emura, H. Sawada, S. Araki, N. Harada
We propose a frequency-domain blind source separation method that uses (a) the ℓ1 norm of orthonormal vectors of estimated source signals as a sparsity measure and (b) Cayley transform for optimizing the objective function under the unitary constraint in the Riemannian geometry approach. The orthonormal vectors of estimated source signals, obtained by the sphering of observed mixed signals and the unitary constraint on the separation filters, enables us to use the ℓ1 norm properly as a sparsity measure. The Cayley transform enables us to handle the geometrical aspects of the unitary constraint efficiently. According to the simulation of a two-channel case, the proposed method achieved a 20-dB improvement in the source-to-interference ratio in a room with a reverberation time of T60 = 300ms.
{"title":"A Frequency-Domain BSS Method Based on ℓ1 Norm, Unitary Constraint, and Cayley Transform","authors":"S. Emura, H. Sawada, S. Araki, N. Harada","doi":"10.1109/ICASSP40776.2020.9053757","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053757","url":null,"abstract":"We propose a frequency-domain blind source separation method that uses (a) the ℓ1 norm of orthonormal vectors of estimated source signals as a sparsity measure and (b) Cayley transform for optimizing the objective function under the unitary constraint in the Riemannian geometry approach. The orthonormal vectors of estimated source signals, obtained by the sphering of observed mixed signals and the unitary constraint on the separation filters, enables us to use the ℓ1 norm properly as a sparsity measure. The Cayley transform enables us to handle the geometrical aspects of the unitary constraint efficiently. According to the simulation of a two-channel case, the proposed method achieved a 20-dB improvement in the source-to-interference ratio in a room with a reverberation time of T60 = 300ms.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"48 1","pages":"111-115"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77148137","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9054758
Yaxian Xia, Lun Huang, Wenmin Wang, Xiao-Yong Wei, Jie Chen
Exploring the entity-level (i.e., objects in an image, words in a text) spatial relationship contributes to understanding multimedia content precisely. The ignorance of spatial information in previous works probably leads to misunderstandings of image contents. For instance, sentences ‘Boats are on the water’ and ‘Boats are under the water’ describe the same objects, but correspond to different sceneries. To this end, we utilize the relative position of objects to capture entity-level spatial relationships for image-text matching. Specifically, we fuse semantic and spatial relationships of image objects in a visual intra-modal relation module. The module performs promisingly to understand image contents and improve object representation learning. It contributes to capturing entity-level latent correspondence of image-text pairs. Then the query (text) plays a role of textual context to refine the interpretable alignments of image-text pairs in the inter-modal relation module. Our proposed method achieves state-of-the-art results on MSCOCO and Flickr30K datasets.
{"title":"Exploring Entity-Level Spatial Relationships for Image-Text Matching","authors":"Yaxian Xia, Lun Huang, Wenmin Wang, Xiao-Yong Wei, Jie Chen","doi":"10.1109/ICASSP40776.2020.9054758","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054758","url":null,"abstract":"Exploring the entity-level (i.e., objects in an image, words in a text) spatial relationship contributes to understanding multimedia content precisely. The ignorance of spatial information in previous works probably leads to misunderstandings of image contents. For instance, sentences ‘Boats are on the water’ and ‘Boats are under the water’ describe the same objects, but correspond to different sceneries. To this end, we utilize the relative position of objects to capture entity-level spatial relationships for image-text matching. Specifically, we fuse semantic and spatial relationships of image objects in a visual intra-modal relation module. The module performs promisingly to understand image contents and improve object representation learning. It contributes to capturing entity-level latent correspondence of image-text pairs. Then the query (text) plays a role of textual context to refine the interpretable alignments of image-text pairs in the inter-modal relation module. Our proposed method achieves state-of-the-art results on MSCOCO and Flickr30K datasets.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"429 1","pages":"4452-4456"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77238665","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9054645
Jingran Su, Yang Zhao, Xuelong Li
Gait information has gradually attracted people’s attention duing to its uniqueness. Methods based on deep metric learning are successfully utlized in gait recognition tasks. However, most of the previous studies use losses which only consider a small number of samples in the mini-batch, such as Triplet loss and Quadruplet Loss, which is not conducive to the convergence of the model. Therefore, in this paper, a novel loss named Center-ranked is proposed to integrate all positive and negative samples information. We also propose a simple model for gait recognition tasks to verify the validity of the loss. Extensive experiments on two challenging datasets CASIA-B and OU-MVLP demonstrate the superiority and effectiveness of our proposed Center-ranked loss and model.
{"title":"Deep Metric Learning Based On Center-Ranked Loss for Gait Recognition","authors":"Jingran Su, Yang Zhao, Xuelong Li","doi":"10.1109/ICASSP40776.2020.9054645","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9054645","url":null,"abstract":"Gait information has gradually attracted people’s attention duing to its uniqueness. Methods based on deep metric learning are successfully utlized in gait recognition tasks. However, most of the previous studies use losses which only consider a small number of samples in the mini-batch, such as Triplet loss and Quadruplet Loss, which is not conducive to the convergence of the model. Therefore, in this paper, a novel loss named Center-ranked is proposed to integrate all positive and negative samples information. We also propose a simple model for gait recognition tasks to verify the validity of the loss. Extensive experiments on two challenging datasets CASIA-B and OU-MVLP demonstrate the superiority and effectiveness of our proposed Center-ranked loss and model.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"153 1","pages":"4077-4081"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80996845","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9053699
Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cei, Youjun Xiang, Yuli Fu
3D Morphable Model (3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address this problem, Two-Pathway Encoder-Decoder Network (2PEDN) is proposed to regress the identity and expression components via global and local pathways. Specifically, each 2D face image is cropped into global face and local details as the inputs for the corresponding pathways. 2PEDN is trained to predict 3D face shape components with two sets of loss functions designed to supervise 3D face reconstruction error and face identification error. To reduce the conflict between abundant facial details and saving computer storage space, a magnitudes converter is devised. Experiments demonstrate that the proposed method outperforms several 3D face recontruction methods.
{"title":"A Novel Two-Pathway Encoder-Decoder Network for 3D Face Reconstruction","authors":"Xianfeng Li, Zichun Weng, Juntao Liang, Lei Cei, Youjun Xiang, Yuli Fu","doi":"10.1109/ICASSP40776.2020.9053699","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053699","url":null,"abstract":"3D Morphable Model (3DMM) is a statistical tool widely employed in reconstructing 3D face shape. Existing methods are aimed at predicting 3DMM shape parameters with a single encoder but suffer from unclear distinction of different attributes. To address this problem, Two-Pathway Encoder-Decoder Network (2PEDN) is proposed to regress the identity and expression components via global and local pathways. Specifically, each 2D face image is cropped into global face and local details as the inputs for the corresponding pathways. 2PEDN is trained to predict 3D face shape components with two sets of loss functions designed to supervise 3D face reconstruction error and face identification error. To reduce the conflict between abundant facial details and saving computer storage space, a magnitudes converter is devised. Experiments demonstrate that the proposed method outperforms several 3D face recontruction methods.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"186 1","pages":"3682-3686"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81077269","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 : 2020-05-01DOI: 10.1109/ICASSP40776.2020.9053556
Robin Scheibler, Nobutaka Ono
We propose a new algorithm for the blind source separation of acoustic sources. This algorithm is an alternative to the popular auxiliary function based independent vector analysis using iterative projection (AuxIVA-IP). It optimizes the same cost function, but instead of alternate updates of the rows of the demixing matrix, we propose a sequence of rank-1 updates. Remarkably, and unlike the previous method, the resulting updates do not require matrix inversion. Moreover, their computational complexity is quadratic in the number of microphones, rather than cubic in AuxIVA-IP. In addition, we show that the new method can be derived as alternate updates of the steering vectors of sources. Accordingly, we name the method iterative source steering (AuxIVA-ISS). Finally, we confirm in simulated experiments that the proposed algorithm separates sources just as well as AuxIVA-IP, at a lower computational cost.
{"title":"Fast and Stable Blind Source Separation with Rank-1 Updates","authors":"Robin Scheibler, Nobutaka Ono","doi":"10.1109/ICASSP40776.2020.9053556","DOIUrl":"https://doi.org/10.1109/ICASSP40776.2020.9053556","url":null,"abstract":"We propose a new algorithm for the blind source separation of acoustic sources. This algorithm is an alternative to the popular auxiliary function based independent vector analysis using iterative projection (AuxIVA-IP). It optimizes the same cost function, but instead of alternate updates of the rows of the demixing matrix, we propose a sequence of rank-1 updates. Remarkably, and unlike the previous method, the resulting updates do not require matrix inversion. Moreover, their computational complexity is quadratic in the number of microphones, rather than cubic in AuxIVA-IP. In addition, we show that the new method can be derived as alternate updates of the steering vectors of sources. Accordingly, we name the method iterative source steering (AuxIVA-ISS). Finally, we confirm in simulated experiments that the proposed algorithm separates sources just as well as AuxIVA-IP, at a lower computational cost.","PeriodicalId":13127,"journal":{"name":"ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","volume":"51 1","pages":"236-240"},"PeriodicalIF":0.0,"publicationDate":"2020-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81126826","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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