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

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

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

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

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

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

Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method 利用静息状态功能网络连接区分精神分裂症和正常对照:一种深度神经网络和分层相关传播方法

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168179

Weizheng Yan, S. Plis, V. Calhoun, Shengfeng Liu, R. Jiang, T. Jiang, J. Sui

{"title":"Discriminating schizophrenia from normal controls using resting state functional network connectivity: A deep neural network and layer-wise relevance propagation method","authors":"Weizheng Yan, S. Plis, V. Calhoun, Shengfeng Liu, R. Jiang, T. Jiang, J. Sui","doi":"10.1109/MLSP.2017.8168179","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168179","url":null,"abstract":"Deep learning has gained considerable attention in the scientific community, breaking benchmark records in many fields such as speech and visual recognition [1]. Motivated by extending advancement of deep learning approaches to brain imaging classification, we propose a framework, called “deep neural network (DNN)+ layer-wise relevance propagation (LRP)”, to distinguish schizophrenia patients (SZ) from healthy controls (HCs) using functional network connectivity (FNC). 1100 Chinese subjects of 7 sites are included, each with a 50∗50 FNC matrix resulted from group ICA on resting-state fMRI data. The proposed DNN+LRP not only improves classification accuracy significantly compare to four state-of-the-art classification methods (84% vs. less than 79%, 10 folds cross validation) but also enables identification of the most contributing FNC patterns related to SZ classification, which cannot be easily traced back by general DNN models. By conducting LRP, we identified the FNC patterns that exhibit the highest discriminative power in SZ classification. More importantly, when using leave-one-site-out cross validation (using 6 sites for training, 1 site for testing, 7 times in total), the cross-site classification accuracy reached 82%, suggesting high robustness and generalization performance of the proposed method, promising a wide utility in the community and great potentials for biomarker identification of brain disorders.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"14 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":"75381907","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: 40

MIML-AI: Mixed-supervision multi-instance multi-label learning with auxiliary information MIML-AI:带辅助信息的混合监督多实例多标签学习

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168107

Tarn Nguyen, R. Raich, Xiaoli Z. Fern, Anh T. Pham

引用次数: 0

Infinite probabilistic latent component analysis for audio source separation 音频源分离的无限概率潜在分量分析

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168189

Kazuyoshi Yoshii, Eita Nakamura, Katsutoshi Itoyama, Masataka Goto

{"title":"Infinite probabilistic latent component analysis for audio source separation","authors":"Kazuyoshi Yoshii, Eita Nakamura, Katsutoshi Itoyama, Masataka Goto","doi":"10.1109/MLSP.2017.8168189","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168189","url":null,"abstract":"This paper presents a statistical method of audio source separation based on a nonparametric Bayesian extension of probabilistic latent component analysis (PLCA). A major approach to audio source separation is to use nonnegative matrix factorization (NMF) that approximates the magnitude spectrum of a mixture signal at each frame as the weighted sum of fewer source spectra. Another approach is to use PLCA that regards the magnitude spectrogram as a two-dimensional histogram of “sound quanta” and classifies each quantum into one of sources. While NMF has a physically-natural interpretation, PLCA has been used successfully for music signal analysis. To enable PLCA to estimate the number of sources, we propose Dirichlet process PLCA (DP-PLCA) and derive two kinds of learning methods based on variational Bayes and collapsed Gibbs sampling. Unlike existing learning methods for nonparametric Bayesian NMF based on the beta or gamma processes (BP-NMF and GaP-NMF), our sampling method can efficiently search for the optimal number of sources without truncating the number of sources to be considered. Experimental results showed that DP-PLCA is superior to GaP-NMF in terms of source number estimation.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"15 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":"87005228","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Difference-of-Convex optimization for variational kl-corrected inference in dirichlet process mixtures dirichlet过程混合中变分kl校正推理的凸差分优化

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168159

Rasmus Bonnevie, Mikkel N. Schmidt, Morten Mørup

引用次数: 0