2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)最新文献_第6页

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

Text to image generative model using constrained embedding space mapping 文本到图像的约束嵌入空间映射生成模型

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168111

Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar, Md. A. Salam Khan, Ryuki Tachibana

{"title":"Text to image generative model using constrained embedding space mapping","authors":"Subhajit Chaudhury, Sakyasingha Dasgupta, Asim Munawar, Md. A. Salam Khan, Ryuki Tachibana","doi":"10.1109/MLSP.2017.8168111","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168111","url":null,"abstract":"We present a conditional generative method that maps low-dimensional embeddings of image and natural language to a common latent space hence extracting semantic relationships between them. The embedding specific to a modality is first extracted and subsequently a constrained optimization procedure is performed to project the two embedding spaces to a common manifold. Based on this, we present a method to learn the conditional probability distribution of the two embedding spaces; first, by mapping them to a shared latent space and generating back the individual embeddings from this common space. However, in order to enable independent conditional inference for separately extracting the corresponding embeddings from the common latent space representation, we deploy a proxy variable trick — wherein, the single shared latent space is replaced by two separate latent spaces. We design an objective function, such that, during training we can force these separate spaces to lie close to each other, by minimizing the Euclidean distance between their distribution functions. Experimental results demonstrate that the learned joint model can generalize to learning concepts of double MNIST digits with additional attributes of colors, thereby enabling the generation of specific colored images from the respective text data.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"62 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":"85222872","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: 5

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

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

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

Semi-Blind speech enhancement basedon recurrent neural network for source separation and dereverberation 基于递归神经网络的半盲语音增强源分离与去噪

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168191

Masaya Wake, Yoshiaki Bando, M. Mimura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara

{"title":"Semi-Blind speech enhancement basedon recurrent neural network for source separation and dereverberation","authors":"Masaya Wake, Yoshiaki Bando, M. Mimura, Katsutoshi Itoyama, Kazuyoshi Yoshii, Tatsuya Kawahara","doi":"10.1109/MLSP.2017.8168191","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168191","url":null,"abstract":"This paper describes a semi-blind speech enhancement method using a semi-blind recurrent neural network (SB-RNN) for human-robot speech interaction. When a robot interacts with a human using speech signals, the robot inputs not only audio signals recorded by its own microphone but also speech signals made by the robot itself, which can be used for semi-blind speech enhancement. The SB-RNN consists of cascaded two modules: a semi-blind source separation module and a blind dereverberation module. Each module has a recurrent layer to capture the temporal correlations of speech signals. The SB-RNN is trained in a manner of multi-task learning, i.e., isolated echoic speech signals are used as teacher signals for the output of the separation module in addition to isolated unechoic signals for the output of the dereverberation module. Experimental results showed that the source to distortion ratio was improved by 2.30 dB on average compared to a conventional method based on a semi-blind independent component analysis. The results also showed the effectiveness of modularization of the network, multi-task learning, the recurrent structure, and semi-blind source separation.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"98 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":"76982569","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

A Bayesian forecasting and anomaly detection framework for vehicular monitoring networks 车辆监测网络的贝叶斯预测与异常检测框架

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168151

Maria Scalabrin, Matteo Gadaleta, Riccardo Bonetto, M. Rossi

{"title":"A Bayesian forecasting and anomaly detection framework for vehicular monitoring networks","authors":"Maria Scalabrin, Matteo Gadaleta, Riccardo Bonetto, M. Rossi","doi":"10.1109/MLSP.2017.8168151","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168151","url":null,"abstract":"In this paper, we are concerned with the automated and runtime analysis of vehicular data from large scale traffic monitoring networks. This problem is tackled through localized and small-size Bayesian networks (BNs), which are utilized to capture the spatio-temporal relationships underpinning traffic data from nearby road links. A dedicated BN is set up, trained, and tested for each road in the monitored geographical map. The joint probability distribution between the cause nodes and the effect node in the BN is tracked through a Gaussian Mixture Model (GMM), whose parameters are estimated via Bayesian Variational Inference (BVI). Forecasting and anomaly detection are performed on statistical measures derived at runtime by the trained GMMs. Our design choices lead to several advantages: the approach is scalable as a small-size BN is associated with and independently trained for each road and the localized nature of the framework allows flagging atypical behaviors at their point of origin in the monitored geographical map. The effectiveness of the proposed framework is tested using a large dataset from a real network deployment, comparing its prediction performance with that of selected regression algorithms from the literature, while also quantifying its anomaly detection capabilities.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"123 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":"83473001","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: 12

Unsupervised domain adaptation with copula models copula模型的无监督域自适应

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168131

Cuong D. Tran, Ognjen Rudovic, V. Pavlovic

引用次数: 3