2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)最新文献_第4页

Object classification with convolution neural network based on the time-frequency representation of their echo 基于回声时频表示的卷积神经网络目标分类

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168134

Mariia Dmitrieva, Matias Valdenegro-Toro, K. Brown, G. Heald, D. Lane

引用次数: 13

Speech recognition features based on deep latent Gaussian models 基于深隐高斯模型的语音识别特征

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168174

Andros Tjandra, S. Sakti, Satoshi Nakamura

引用次数: 0

A linear stochastic state space model for electrocardiograms 心电图的线性随机状态空间模型

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168126

Kimmo Suotsalo, S. Särkkä

引用次数: 3

Learning embeddings for speaker clustering based on voice equality 基于语音平等的说话人聚类学习嵌入

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168166

Y. X. Lukic, Carlo Vogt, Oliver Durr, Thilo Stadelmann

{"title":"Learning embeddings for speaker clustering based on voice equality","authors":"Y. X. Lukic, Carlo Vogt, Oliver Durr, Thilo Stadelmann","doi":"10.1109/MLSP.2017.8168166","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168166","url":null,"abstract":"Recent work has shown that convolutional neural networks (CNNs) trained in a supervised fashion for speaker identification are able to extract features from spectrograms which can be used for speaker clustering. These features are represented by the activations of a certain hidden layer and are called embeddings. However, previous approaches require plenty of additional speaker data to learn the embedding, and although the clustering results are then on par with more traditional approaches using MFCC features etc., room for improvements stems from the fact that these embeddings are trained with a surrogate task that is rather far away from segregating unknown voices — namely, identifying few specific speakers. We address both problems by training a CNN to extract embeddings that are similar for equal speakers (regardless of their specific identity) using weakly labeled data. We demonstrate our approach on the well-known TIMIT dataset that has often been used for speaker clustering experiments in the past. We exceed the clustering performance of all previous approaches, but require just 100 instead of 590 unrelated speakers to learn an embedding suited for clustering.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"347 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":"77784383","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: 13

Neonatal seizure detection using convolutional neural networks 基于卷积神经网络的新生儿癫痫检测

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168193

Alison O'Shea, G. Lightbody, G. Boylan, A. Temko

引用次数: 40

Detecting malignant ventricular arrhythmias in electrocardiograms by Gaussian process classification 用高斯过程分类检测心电图中的恶性室性心律失常

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168160

Kimmo Suotsalo, S. Särkkä

引用次数: 5

Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity 使用基于脑网络图谱的功能连通性预测个性化智商分数

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168150

R. Jiang, S. Qi, Yuhui Du, Weizheng Yan, V. Calhoun, T. Jiang, J. Sui

{"title":"Predicting individualized intelligence quotient scores using brainnetome-atlas based functional connectivity","authors":"R. Jiang, S. Qi, Yuhui Du, Weizheng Yan, V. Calhoun, T. Jiang, J. Sui","doi":"10.1109/MLSP.2017.8168150","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168150","url":null,"abstract":"Variation in several brain regions and neural parameters is associated with intelligence. In this study, we adopted functional connectivity (FC) based on Brainnetome-atlas to predict the intelligence quotient (IQ) scores quantitatively with a prediction framework incorporating advanced feature selection and regression methods. We compared prediction performance of five regression models and evaluated the effectiveness of feature selection. The best prediction performance was achieved by ReliefF+LASSO, by which correlations of r=0.72 and r=0.46 between prediction and true values were obtained for 174 female and 186 male subjects respectively in a leave-one-out-cross-validation, suggesting that for female subjects, a better prediction of IQ scores can be achieved using precise FCs. Further, weight analysis revealed the most predictive FCs and the relevant regions. Results support the hypothesis that intelligence is characterized by interaction between multiple brain regions, especially the parieto-frontal integration theory implicated areas. This study facilitates our understanding of the biological basis of intelligence by individualized prediction.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"60 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":"86376970","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

Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components 基于功能独立分量的核支持向量机判别双相情感障碍与重度抑郁症

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168110

Shuang Gao, E. Osuch, M. Wammes, J. Théberge, T. Jiang, V. Calhoun, J. Sui

{"title":"Discriminating bipolar disorder from major depression based on kernel SVM using functional independent components","authors":"Shuang Gao, E. Osuch, M. Wammes, J. Théberge, T. Jiang, V. Calhoun, J. Sui","doi":"10.1109/MLSP.2017.8168110","DOIUrl":"https://doi.org/10.1109/MLSP.2017.8168110","url":null,"abstract":"Bipolar disorder (BD) and major depressive disorder (MDD) both share depressive symptoms, so how to discriminate them in early depressive episodes is a major clinical challenge. Independent components (ICs) extracted from fMRI data have been proved to carry distinguishing information and can be used for classification. Here we extend a previous method that makes use of multiple fMRI ICs to build linear subspaces for each individual, which is further used as input for classifiers. The similarity matrix between different subjects is first calculated using distance metric of principal angle, which is then projected into kernel space for support vector machine (SVM) classification among 37 BDs and 36 MDDs. In practice, we adopt forward selection technique on 20 ICs and nested 10-fold cross validation to select the most discriminative IC combinations of fMRI and determine the final diagnosis by majority voting mechanism. The results on human data demonstrate that the proposed method achieves much better performance than its initial version [8] (93% vs. 75%), and identifies 5 discriminative fMRI components for distinguishing BD and MDD patients, which are mainly located in prefrontal cortex, default mode network and thalamus etc. This work provides a new framework for helping diagnose the new patients with overlapped symptoms between BD and MDD, which not only adds to our understanding of functional deficits in mood disorders, but also may serve as potential biomarkers for their differential diagnosis.","PeriodicalId":6542,"journal":{"name":"2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP)","volume":"9 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":"87577059","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: 14

Gaussian density guided deep neural network for single-channel speech enhancement 基于高斯密度的深度神经网络单通道语音增强

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168116

Li Chai, Jun Du, Yannan Wang

引用次数: 11

An accelerated newton's method for projections onto the ℓ1-ball 用加速牛顿法求球面上的投影

2017 IEEE 27th International Workshop on Machine Learning for Signal Processing (MLSP) Pub Date : 2017-09-01 DOI: 10.1109/MLSP.2017.8168161

P. Rodríguez

引用次数: 7