2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)最新文献_第6页

Hierarchical Traffic Matrices: Axiomatic Foundations to Practical Traffic Matrix Synthesis 层次交通矩阵:实用交通矩阵合成的公理基础

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659593

Paul Tune, M. Roughan, Chris Wiren

引用次数: 0

EEG Hyperscanning for Eight or more Persons - Feasibility Study for Emotion Recognition using Deep Learning Technique 八人或以上的脑电图超扫描-使用深度学习技术进行情绪识别的可行性研究

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659738

Sunghan Lee, Sangjun Han, S. Jun

引用次数: 2

User's Intention Understanding in Question-Answering System Using Attention-based LSTM 基于注意力的LSTM问答系统中的用户意图理解

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659636

Yukio Matsuyoshi, T. Takiguchi, Y. Ariki

引用次数: 1

A study on impulse response model of reflected path for THz band 太赫兹波段反射路径脉冲响应模型的研究

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659776

Kazuhiro Tsujimura, K. Umebayashi, J. Kokkoniemi, Janne J. Lehtomäki

引用次数: 3

XOR learning by spiking neural network with infrared communications 利用脉冲神经网络与红外通信进行异或学习

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659484

Kazuki Matsumoto, H. Torikai, H. Sekiya

引用次数: 1

Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging 医学深度学习的图形用户界面-应用于磁共振成像

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659515

Sebastian Milde, Annika Liebgott, Ziwei Wu, Wenyi Feng, Jiahuan Yang, Lukas Mauch, P. Martirosian, F. Bamberg, K. Nikolaou, S. Gatidis, F. Schick, Bin Yang, Thomas Kustner

{"title":"Graphical User Interface for Medical Deep Learning - Application to Magnetic Resonance Imaging","authors":"Sebastian Milde, Annika Liebgott, Ziwei Wu, Wenyi Feng, Jiahuan Yang, Lukas Mauch, P. Martirosian, F. Bamberg, K. Nikolaou, S. Gatidis, F. Schick, Bin Yang, Thomas Kustner","doi":"10.23919/APSIPA.2018.8659515","DOIUrl":"https://doi.org/10.23919/APSIPA.2018.8659515","url":null,"abstract":"In clinical diagnostic, magnetic resonance imaging (MRI) is a valuable and versatile tool. The acquisition process is, however, susceptible to image distortions (artifacts) which may lead to degradation of image quality. Automated and reference-free localization and quantification of artifacts by employing convolutional neural networks (CNNs) is a promising way for early detection of artifacts. Training relies on high amount of expert labeled data which is a time-demanding process. Previous studies were based on global labels, i.e. a whole volume was automatically labeled as artifact-free or artifact-affected. However, artifact appearance is rather localized. We propose a local labeling which is conducted via a graphical user interface (GUI). Moreover, the GUI provides easy handling of data viewing, preprocessing (labeling, patching, data augmentation), network parametrization and training, data and network evaluation as well as deep visualization of the learned network content. The GUI is not limited to these features and will be extended in the future. The developed GUI is made publicly available and features a modular outline to target different applications of machine learning and deep learning, such as artifact detection, classification and segmentation.","PeriodicalId":287799,"journal":{"name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114129515","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

Age and Gender Prediction from Face Images Using Convolutional Neural Network 基于卷积神经网络的人脸图像年龄和性别预测

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659655

Koichi Ito, Hiroya Kawai, Takehisa Okano, T. Aoki

引用次数: 22

A DNN-based emotional speech synthesis by speaker adaptation 基于说话人自适应的dnn情绪语音合成

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659599

Hongwu Yang, Weizhao Zhang, Pengpeng Zhi

{"title":"A DNN-based emotional speech synthesis by speaker adaptation","authors":"Hongwu Yang, Weizhao Zhang, Pengpeng Zhi","doi":"10.23919/APSIPA.2018.8659599","DOIUrl":"https://doi.org/10.23919/APSIPA.2018.8659599","url":null,"abstract":"The paper proposes a deep neural network (DNN)-based emotional speech synthesis method to improve the quality of synthesized emotional speech by speaker adaptation with a multi-speaker and multi-emotion speech corpus. Firstly, a text analyzer is employed to obtain the contextual labels from sentences while the WORLD vocoder is used to extract the acoustic features from corresponding speeches. Then a set of speaker-independent DNN average voice models are trained with the contextual labels and acoustic features of multi-emotion speech corpus. Finally, the speaker adaptation is adopted to train a set of speaker-dependent DNN voice models of target emotion with target emotional training speeches. The target emotional speech is synthesized by the speaker-dependent DNN voice models. Subjective evaluations show that comparing with the traditional hidden Markov model (HMM)-based method, the proposed method can achieve higher opinion scores. Objective tests demonstrate that the spectrum of the emotional speech synthesized by the proposed method is also closer to the original speech than that of the emotional speech synthesized by the HMM-based method. Therefore, the proposed method can improve the emotion express and naturalness of synthesized emotional speech.","PeriodicalId":287799,"journal":{"name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116777523","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

Epileptic Focus Localization Based on iEEG by Using Positive Unlabeled (PU) Learning 基于正性无标记学习的iEEG癫痫病灶定位

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659747

Xuyang Zhao, Toshihisa Tanaka, Wanzeng Kong, Qibin Zhao, Jianting Cao, H. Sugano, Noboru Yoshida

{"title":"Epileptic Focus Localization Based on iEEG by Using Positive Unlabeled (PU) Learning","authors":"Xuyang Zhao, Toshihisa Tanaka, Wanzeng Kong, Qibin Zhao, Jianting Cao, H. Sugano, Noboru Yoshida","doi":"10.23919/APSIPA.2018.8659747","DOIUrl":"https://doi.org/10.23919/APSIPA.2018.8659747","url":null,"abstract":"Epilepsy is a chronic disorder of the brain. Intracranial electroencephalogram (iEEG) recorded from cortex is the most popular measurement for not only the diagnosis of epilepsy, but also the focus localization that is crucial for the surgery. In recent years, the machine learning methods have been rapidly developed and applied successfully to various real world problems. Given sufficient number of samples, the powerful deep learning methods can achieve high performance for epileptic focus localization. However, it is a challenging task to obtain large amount of labeled iEEG regarding focal/non-focal channels, since the annotations must be performed by multiple clinical experts through visual judgment on the long term iEEG signals. In order to reduce the necessary number of labeled training samples, we introduce the positive unlabeled (PU) learning method for classification of focal and non-focal epileptic iEEG signals. The proposed method enables us to learn a binary classifier by using small amount of labeled data containing only one class (i.e., focal signals) and unlabeled data containing two classes (i.e., focal and non-focal signals), which greatly reduces the workload of clinical experts for annotations. Experimental results on Bern dataset and iEEG recorded from Juntendo University Hospital demonstrate the effectiveness of our method.","PeriodicalId":287799,"journal":{"name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"43 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113941982","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: 6

Significance of Teager Energy Operator Phase for Replay Spoof Detection 能量算子相位对重放欺骗检测的意义

2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2018-11-01 DOI: 10.23919/APSIPA.2018.8659664

Prasad A. Tapkir, H. Patil

{"title":"Significance of Teager Energy Operator Phase for Replay Spoof Detection","authors":"Prasad A. Tapkir, H. Patil","doi":"10.23919/APSIPA.2018.8659664","DOIUrl":"https://doi.org/10.23919/APSIPA.2018.8659664","url":null,"abstract":"The increased use of voice biometrics for various security applications, motivated authors to investigate different countermeasures for the hazard of spoofing attacks, where the attacker tries to imitate the genuine speaker. The replay is the most accessible spoofing attack. Past studies have ignored phase information for various speech processing applications. In this paper, we explore the excitation source-like feature set, namely, Teager Energy Operator (TEO) phase and its significance in the replay spoof detection task. This feature set is further fused at score-level with magnitude spectrum-based features, such as Constant Q Cepstral Coefficients (CQCC), Mel Frequency Cepstral Coefficients (MFCC), and Linear Frequency Cepstral Coefficients (LFCC). The improvement in the results show that the TEO phase feature set contains the complementary information to the magnitude spectrum-based features. The experiments are performed on the ASV Spoof 2017 Challenge database. The systems are implemented with Gaussian Mixture Model (GMM) as a classifier. Our best system using TEO phase achieves the Equal Error Rate (EER) of 6.57% and 15.39% on the development and evaluation set, respectively.","PeriodicalId":287799,"journal":{"name":"2018 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122502610","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: 4