IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing最新文献_第2页

INFORMATION THEORETIC FEATURE PROJECTION FOR SINGLE-TRIAL BRAIN-COMPUTER INTERFACES. 单次试验脑机接口的信息理论特征投影。

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2017-09-01 Epub Date: 2017-12-07 DOI: 10.1109/MLSP.2017.8168178

Ozan Özdenizci, Fernando Quivira, Deniz Erdoğmuş

引用次数: 4

VOWEL DURATION MEASUREMENT USING DEEP NEURAL NETWORKS. 利用深度神经网络测量元音持续时间

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2015-09-01 Epub Date: 2015-11-12 DOI: 10.1109/MLSP.2015.7324331

Yossi Adi, Joseph Keshet, Matthew Goldrick

引用次数: 0

Spatial stochastic process clustering using a local a posteriori probability 使用局部后验概率的空间随机过程聚类

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2014-11-20 DOI: 10.1109/MLSP.2014.6958850

E. Grall-Maës

引用次数: 2

INFERRING CLINICAL DEPRESSION FROM SPEECH AND SPOKEN UTTERANCES. 从言语和口语中推断临床抑郁症。

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2014-09-01 Epub Date: 2014-11-20 DOI: 10.1109/mlsp.2014.6958856

Meysam Asgari, Izhak Shafran, Lisa B Sheeber

{"title":"INFERRING CLINICAL DEPRESSION FROM SPEECH AND SPOKEN UTTERANCES.","authors":"Meysam Asgari, Izhak Shafran, Lisa B Sheeber","doi":"10.1109/mlsp.2014.6958856","DOIUrl":"10.1109/mlsp.2014.6958856","url":null,"abstract":"In this paper, we investigate the problem of detecting depression from recordings of subjects' speech using speech processing and machine learning. There has been considerable interest in this problem in recent years due to the potential for developing objective assessments from real-world behaviors, which may provide valuable supplementary clinical information or may be useful in screening. The cues for depression may be present in \"what is said\" (content) and \"how it is said\" (prosody). Given the limited amounts of text data, even in this relatively large study, it is difficult to employ standard method of learning models from n-gram features. Instead, we learn models using word representations in an alternative feature space of valence and arousal. This is akin to embedding words into a real vector space albeit with manual ratings instead of those learned with deep neural networks [1]. For extracting prosody, we employ standard feature extractors such as those implemented in openSMILE and compare them with features extracted from harmonic models that we have been developing in recent years. Our experiments show that our features from harmonic model improve the performance of detecting depression from spoken utterances than other alternatives. The context features provide additional improvements to achieve an accuracy of about 74%, sufficient to be useful in screening applications.","PeriodicalId":73290,"journal":{"name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","volume":"2014 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7719299/pdf/nihms-1648882.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38686607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

INFERRING SOCIAL CONTEXTS FROM AUDIO RECORDINGS USING DEEP NEURAL NETWORKS. 利用深度神经网络从录音中推断社会背景。

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2014-09-01 Epub Date: 2014-11-20 DOI: 10.1109/MLSP.2014.6958853

Meysam Asgari, Izhak Shafran, Alireza Bayestehtashk

引用次数: 0

Inter-document reference detection as an alternative to full text semantic analysis in document clustering 文档间引用检测作为文档聚类中全文语义分析的替代方法

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2013-01-01 DOI: 10.1109/MLSP.2013.6661952

P. D. Mazière, M. Hulle

{"title":"Inter-document reference detection as an alternative to full text semantic analysis in document clustering","authors":"P. D. Mazière, M. Hulle","doi":"10.1109/MLSP.2013.6661952","DOIUrl":"https://doi.org/10.1109/MLSP.2013.6661952","url":null,"abstract":"We discuss here the search for inter-document references as an alternative to the grouping of document inventories based on a full text semantic analysis. The used document inventory, which is not publicly available, was provided to us by the European Union (EU) in the framework of an EU project, the aim of which was to analyse, classify, and visualise EU funded research in social sciences and humanities in EU framework programmes FP5 and FP6. This project, called the SSH project for short, was aimed at the evaluation of the contributions of research to the development of EU policies. For the semantic based grouping, we start from a Multi-Dimensional Scaling analysis of the document vectors, which is the result of a prior semantic analysis. As an alternative to a semantic analysis, we searched for inter-document references or direct references. Direct references are defined as terms that explicitly refer to other documents present in the inventory. We show that the grouping based on references is largely similar to the one based on semantics, but with considerably less computational efforts. In addition, the non-expert can make better use of the results, since the references are displayed as graphical webpages with hyperlinks pointing to both the referenced and the referencing document(s), and the reason of linkage. Finally, we show that the combination of a database, to store the data and the (intermediate) results, and a webserver, to visualise the results, offers a powerful platform to analyse the document inventory and to share the results with all participants/collaborators involved in a data- and computation intensive EU-project, thereby guaranteeing both data- and result-consistency.","PeriodicalId":73290,"journal":{"name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","volume":"75 1","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88002457","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

CONSTRAINED SPECTRAL CLUSTERING FOR IMAGE SEGMENTATION. 约束光谱聚类的图像分割。

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-12-31 DOI: 10.1109/MLSP.2012.6349765

Jamshid Sourati, Dana H Brooks, Jennifer G Dy, Deniz Erdogmus

引用次数: 3

Level Sets for Retinal Vasculature Segmentation Using Seeds from Ridges and Edges from Phase Maps. 基于相位图脊和边缘种子的视网膜血管分割水平集。

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-01-01 DOI: 10.1109/MLSP.2012.6349730

Bekir Dizdaroğlu, Esra Ataer-Cansizoglu, Jayashree Kalpathy-Cramer, Katie Keck, Michael F Chiang, Deniz Erdogmus

{"title":"Level Sets for Retinal Vasculature Segmentation Using Seeds from Ridges and Edges from Phase Maps.","authors":"Bekir Dizdaroğlu, Esra Ataer-Cansizoglu, Jayashree Kalpathy-Cramer, Katie Keck, Michael F Chiang, Deniz Erdogmus","doi":"10.1109/MLSP.2012.6349730","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349730","url":null,"abstract":"In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.","PeriodicalId":73290,"journal":{"name":"IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing","volume":" ","pages":"1-6"},"PeriodicalIF":0.0,"publicationDate":"2012-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/MLSP.2012.6349730","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32463922","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 28

Designing spatial filters based on neuroscience theories to improve error-related potential classification 设计基于神经科学理论的空间滤波器以改进与错误相关的潜在分类

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-01-01 DOI: 10.1109/MLSP.2012.6349740

S. Rousseau, C. Jutten, M. Congedo

引用次数: 2

OBSERVER AND FEATURE ANALYSIS ON DIAGNOSIS OF RETINOPATHY OF PREMATURITY. 早产儿视网膜病变诊断的观察与特征分析。

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-01-01 DOI: 10.1109/MLSP.2012.6349809

E Ataer-Cansizoglu, S You, J Kalpathy-Cramer, K Keck, M F Chiang, D Erdogmus

引用次数: 10