2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)最新文献_第5页

A diagonal plus low-rank covariance model for computationally efficient source separation 一个对角线加低秩协方差模型计算有效的源分离

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168169

A. Liutkus, Kazuyoshi Yoshii

{"title":"A diagonal plus low-rank covariance model for computationally efficient source separation","authors":"A. Liutkus, Kazuyoshi Yoshii","doi":"10.1109/MLSP.2017.8168169","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168169","url":null,"abstract":"This paper presents an accelerated version of positive semidefinite tensor factorization (PSDTF) for blind source separation. PSDTF works better than nonnegative matrix factorization (NMF) by dropping the arguable assumption that audio signals can be whitened in the frequency domain by using short-term Fourier transform (STFT). Indeed, this assumption only holds true in an ideal situation where each frame is infinitely long and the target signal is completely stationary in each frame. PSDTF thus deals with full covariance matrices over frequency bins instead of forcing them to be diagonal as in NMF. Although PSDTF significantly outperforms NMF in terms of separation performance, it suffers from a heavy computational cost due to the repeated inversion of big covariance matrices. To solve this problem, we propose an intermediate model based on diagonal plus low-rank covariance matrices and derive the expectation-maximization (EM) algorithm for efficiently updating the parameters of PSDTF. Experimental results showed that our method can dramatically reduce the complexity of PSDTF by several orders of magnitude without a significant decrease in separation performance.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88310167","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: 8

On generating mixing noise signals with basis functions for simulating noisy speech and learning dnn-based speech enhancement models 基于基函数的混合噪声信号的生成及基于dnn的语音增强模型的学习

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168192

Shi-Xue Wen, Jun Du, Chin-Hui Lee

{"title":"On generating mixing noise signals with basis functions for simulating noisy speech and learning dnn-based speech enhancement models","authors":"Shi-Xue Wen, Jun Du, Chin-Hui Lee","doi":"10.1109/MLSP.2017.8168192","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168192","url":null,"abstract":"We first examine the generalization issue with the noise samples used in training nonlinear mapping functions between noisy and clean speech features for deep neural network (DNN) based speech enhancement. Then an empirical proof is established to explain why the DNN-based approach has a good noise generalization capability provided that a large collection of noise types are included in generating diverse noisy speech samples for training. It is shown that an arbitrary noise signal segment can be well represented by a linear combination of microstructure noise bases. Accordingly, we propose to generate these mixing noise signals by designing a set of compact and analytic noise bases without using any realistic noise types. The experiments demonstrate that this noise generation scheme can yield comparable performance to that using 50 real noise types. Furthermore, by supplementing the collected noise types with the synthesized noise bases, we observe remarkable performance improvements implying that not only a large collection of real-world noise signals can be alleviated, but also a good noise generalization capability can be achieved.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"110 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75628645","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: 3

Partitioning in signal processing using the object migration automaton and the pursuit paradigm 用对象迁移自动机和追踪范式划分信号处理

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168149

Abdolreza Shirvani, B. Oommen

{"title":"Partitioning in signal processing using the object migration automaton and the pursuit paradigm","authors":"Abdolreza Shirvani, B. Oommen","doi":"10.1109/MLSP.2017.8168149","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168149","url":null,"abstract":"Data in all Signal Processing (SP) applications is being generated super-exponentially, and at an ever increasing rate. A meaningful way to pre-process it so as to achieve feasible computation is by Partitioning the data [5]. Indeed, the task of partitioning is one of the most difficult problems in computing, and it has extensive applications in solving real-life problems, especially when the amount of SP data (i.e., images, voices, speakers, libraries etc.) to be processed is prohibitively large. The problem is known to be NP-hard. The benchmark solution for this for the Equi-partitioning Problem (EPP) has involved the classic field of Learning Automata (LA), and the corresponding algorithm, the Object Migrating Automata (OMA) has been used in numerous application domains. While the OMA is a fixed structure machine, it does not incorporate the Pursuit concept that has, recently, significantly enhanced the field of LA. In this paper, we pioneer the incorporation of the Pursuit concept into the OMA. We do this by a non-intuitive paradigm, namely that of removing (or discarding) from the query stream, queries that could be counter-productive. This can be perceived as a filtering agent triggered by a pursuit-based module. The resulting machine, referred to as the Pursuit OMA (POMA), has been rigorously tested in all the standard benchmark environments. Indeed, in certain extreme environments it is almost ten times faster than the original OMA. The application of the POMA to all signal processing applications is extremely promising.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"440 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73598761","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: 2

Mel-Generalized cepstral regularization for discriminative non-negative matrix factorization 判别非负矩阵分解的广义倒谱正则化

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168142

Li Li, H. Kameoka, S. Makino

{"title":"Mel-Generalized cepstral regularization for discriminative non-negative matrix factorization","authors":"Li Li, H. Kameoka, S. Makino","doi":"10.1109/MLSP.2017.8168142","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168142","url":null,"abstract":"The non-negative matrix factorization (NMF) approach has shown to work reasonably well for monaural speech enhancement tasks. This paper proposes addressing two shortcomings of the original NMF approach: (1) the objective functions for the basis training and separation (Wiener filtering) are inconsistent (the basis spectra are not trained so that the separated signal becomes optimal); (2) minimizing spectral divergence measures does not necessarily lead to an enhancement in the feature domain (e.g., cepstral domain) or in terms of perceived quality. To address the first shortcoming, we have previously proposed an algorithm for Discriminative NMF (DNMF), which optimizes the same objective for basis training and separation. To address the second shortcoming, we have previously introduced novel frameworks called the cepstral distance regularized NMF (CDRNMF) and mel-generalized cepstral distance regularized NMF (MGCRNMF), which aim to enhance speech both in the spectral domain and feature domain. This paper proposes combining the goals of DNMF and MGCRNMF by incorporating the MGC regularizer into the DNMF objective function and proposes an algorithm for parameter estimation. The experimental results revealed that the proposed method outperformed the baseline approaches.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"2014 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88132290","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

Navigation-Based learning for survey trajectory classification in autonomous underwater vehicles 基于导航学习的自主水下航行器测量轨迹分类

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168137

M. D. L. Alvarez, H. Hastie, D. Lane

引用次数: 4

Fast algorithm using summed area tables with unified layer performing convolution and average pooling 快速算法使用求和面积表与统一层执行卷积和平均池化

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168154

Akihiko Kasagi, T. Tabaru, H. Tamura

引用次数: 19

Automatic plant identification using stem automata 使用茎自动机的自动植物识别

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168147

Kan Li, Ying Ma, J. Príncipe

引用次数: 3

A comparative study of example-guided audio source separation approaches based on nonnegative matrix factorization 基于非负矩阵分解的实例引导音频源分离方法的比较研究

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168196

A. Ozerov, Srdan Kitic, P. Pérez

引用次数: 1

Correntropy induced metric based common spatial patterns 基于共同空间模式的相关熵诱导度量

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168132

J. Dong, Badong Chen, N. Lu, Haixian Wang, Nanning Zheng

引用次数: 8

Texture classification from single uncalibrated images: Random matrix theory approach 单张未校准图像的纹理分类:随机矩阵理论方法

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168115

E. Nadimi, J. Herp, M. M. Buijs, V. Blanes-Vidal

{"title":"Texture classification from single uncalibrated images: Random matrix theory approach","authors":"E. Nadimi, J. Herp, M. M. Buijs, V. Blanes-Vidal","doi":"10.1109/MLSP.2017.8168115","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168115","url":null,"abstract":"We studied the problem of classifying textured-materials from their single-imaged appearance, under general viewing and illumination conditions, using the theory of random matrices. To evaluate the performance of our algorithm, two distinct databases of images were used: The CUReT database and our database of colorectal polyp images collected from patients undergoing colon capsule endoscopy for early cancer detection. During the learning stage, our classifier algorithm established the universality laws for the empirical spectral density of the largest singular value and normalized largest singular value of the image intensity matrix adapted to the eigenvalues of the information-plus-noise model. We showed that these two densities converge to the generalized extreme value (GEV-Frechet) and Gaussian G1 distribution with rate O(N1/2), respectively. To validate the algorithm, we introduced a set of unseen images to the algorithm. Misclassification rate of approximately 1%–6%, depending on the database, was obtained, which is superior to the reported values of 5%–45% in previous research studies.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"34 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2017-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87945035","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: 2