2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)最新文献_第7页

Dynamic Attention Loss for Small-Sample Image Classification 小样本图像分类的动态注意力损失

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023268

Jie Cao, Yinping Qiu, Dongliang Chang, Xiaoxu Li, Zhanyu Ma

引用次数: 2

Median based Multi-label Prediction by Inflating Emotions with Dyads for Visual Sentiment Analysis 视觉情感分析中基于中位数的多标签情绪膨胀预测

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023303

Tetsuya Asakawa, Masaki Aono

{"title":"Median based Multi-label Prediction by Inflating Emotions with Dyads for Visual Sentiment Analysis","authors":"Tetsuya Asakawa, Masaki Aono","doi":"10.1109/APSIPAASC47483.2019.9023303","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023303","url":null,"abstract":"Visual sentiment analysis investigates sentiment estimation from images and has been an interesting and challenging research problem. Most studies have focused on estimating a few specific sentiments and their intensities. Multi-label sentiment estimation from images has not been sufficiently investigated. The purpose of this research is to accurately estimate the sentiments as a multi-label multi-class problem from given images that evoke multiple different emotions simultaneously. We first introduce the emotion inflation method from six emotions defined by the Emotion6 dataset into 13 emotions (which we call ‘Transf13’) by means of emotional dyads. We then perform multi-label sentiment analysis using the emotion-inflated dataset, where we propose a combined deep neural network model which enables inputs to come from both hand-crafted features (e.g. BoVW (Bag of Visual Words) features) and CNN features. We also introduce a median-based multi-label prediction algorithm, in which we assume that each emotion has a probability distribution. In other words, after training of our deep neural network, we predict the existence of an evoked emotion for a given unknown image if the intensity of the emotion is larger than the median of the corresponding emotion. Experimental results demonstrate that our model outperforms existing state-of-the-art algorithms in terms of subset accuracy.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"5 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134288807","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

Dynamic Adjustment of Railway Emergency Plan Based on Utility Risk Entropy 基于效用风险熵的铁路应急预案动态调整

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023044

Qian Ren, Zhenhai Zhang

引用次数: 0

Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks 基于时间卷积递归神经网络的单通道语音增强

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023013

Jingdong Li, Hui Zhang, Xueliang Zhang, Changliang Li

{"title":"Single Channel Speech Enhancement Using Temporal Convolutional Recurrent Neural Networks","authors":"Jingdong Li, Hui Zhang, Xueliang Zhang, Changliang Li","doi":"10.1109/APSIPAASC47483.2019.9023013","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023013","url":null,"abstract":"In recent decades, neural network based methods have significantly improved the performance of speech enhancement. Most of them estimate time-frequency (T-F) representation of target speech directly or indirectly, then resynthesize waveform using the estimated T-F representation. In this work, we proposed the temporal convolutional recurrent network (TCRN), an end-to-end model that directly map noisy waveform to clean waveform. The TCRN, which is combined convolution and recurrent neural network, is able to efficiently and effectively leverage short-term ang long-term information. Furthermore, we present the architecture that iterately downsample and upsample speech during forward propagation. We show that our model is able to improve the performance of model, compared with existing convolutional recurrent networks. Furthermore, We present several key techniques to stabilize the training process. The experimental results show that our model consistently outperforms existing speech enhancement approaches, in terms of speech intelligibility and quality.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"254 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114649487","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: 7

Dilated-Gated Convolutional Neural Network with A New Loss Function on Sound Event Detection 基于新损失函数的扩展门控卷积神经网络在声音事件检测中的应用

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023308

Ke-Xin He, Weiqiang Zhang, Jia Liu, Yao Liu

引用次数: 1

Automatic Ontology Population Using Deep Learning for Triple Extraction 基于深度学习的三重抽取本体自动填充

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023113

Ming-Hsiang Su, Chung-Hsien Wu, Po-Chen Shih

{"title":"Automatic Ontology Population Using Deep Learning for Triple Extraction","authors":"Ming-Hsiang Su, Chung-Hsien Wu, Po-Chen Shih","doi":"10.1109/APSIPAASC47483.2019.9023113","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023113","url":null,"abstract":"Ontology is a kind of representation used to represent knowledge in a form that computers can derive the content meaning. The purpose of this work is to automatically populate an ontology using deep neural networks for updating an ontology with new facts from an input knowledge resource. In this study for automatic ontology population, a bi-LSTM-based term extraction model based on character embedding is proposed to extract the terms from a sentence. The extracted terms are regarded as the concepts of the ontology. Then, a multi-layer perception network is employed to decide the predicates between the pairs of the extracted concepts. The two concepts (one serves as subject and the other as object) along with the predicate form a triple. The number of occurrences of the dependency relations between the concepts and the predicates are estimated. The predicates with low occurrence frequency are filtered out to obtain precise triples for ontology population. For evaluation of the proposed method, we collected 46,646 sentences from Ontonotes 5.0 for training and testing the bi-LSTM-based term extraction model. We also collected 404,951 triples from ConceptNet 5 for training and testing the multilayer perceptron-based triple extraction model. From the experimental results, the proposed method could extract the triples from the documents, achieving 74.59% accuracy for ontology population.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115260496","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

Robust Camera Model Identification Based on Richer Convolutional Feature Network 基于更丰富卷积特征网络的鲁棒相机模型识别

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023334

Zeyu Zou, Yunxia Liu, Wen-Na Zhang, Yuehui Chen, Yun-Li Zang, Yang Yang, Bonnie Ngai-Fong Law

引用次数: 2

Prosodic Realization of Focus in English by Bidialectal Mandarin Speakers 双语者英语焦点的韵律实现

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023060

Jiajing Zhang, Ying Chen, Jie Cui

引用次数: 1

Can We Simulate Generative Process of Acoustic Modeling Data? Towards Data Restoration for Acoustic Modeling 声学建模数据的生成过程能否模拟?声学建模数据恢复研究

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023184

Ryo Masumura, Yusuke Ijima, Satoshi Kobashikawa, T. Oba, Y. Aono

{"title":"Can We Simulate Generative Process of Acoustic Modeling Data? Towards Data Restoration for Acoustic Modeling","authors":"Ryo Masumura, Yusuke Ijima, Satoshi Kobashikawa, T. Oba, Y. Aono","doi":"10.1109/APSIPAASC47483.2019.9023184","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023184","url":null,"abstract":"In this paper, we present an initial study on data restoration for acoustic modeling in automatic speech recognition (ASR). In the ASR field, the speech log data collected during practical services include customers' personal information, so the log data must often be preserved in segregated storage areas. Our motivation is to permanently and flexibly utilize the log data for acoustic modeling even though the log data cannot be moved from the segregated storage areas. Our key idea is to construct portable models that can simulate the generative process of acoustic modeling data so as to artificially restore the acoustic modeling data. Therefore, this paper proposes novel generative models called acoustic modeling data restorers (AMDRs), that can randomly sample triplets of a phonetic state sequence, an acoustic feature sequence, and utterance attribute information, even if original data is not directly accessible. In order to precisely model the generative process of the acoustic modeling data, we introduce neural language modeling to generate the phonetic state sequences and neural speech synthesis to generate the acoustic feature sequences. Experiments using Japanese speech data sets reveal how close the restored acoustic data is to the original data in terms of ASR performance.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124949432","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

SINGAN: Singing Voice Conversion with Generative Adversarial Networks 歌唱声音转换与生成对抗网络

2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC) Pub Date : 2019-11-01 DOI: 10.1109/APSIPAASC47483.2019.9023162

Berrak Sisman, K. Vijayan, M. Dong, Haizhou Li

{"title":"SINGAN: Singing Voice Conversion with Generative Adversarial Networks","authors":"Berrak Sisman, K. Vijayan, M. Dong, Haizhou Li","doi":"10.1109/APSIPAASC47483.2019.9023162","DOIUrl":"https://doi.org/10.1109/APSIPAASC47483.2019.9023162","url":null,"abstract":"Singing voice conversion (SVC) is a task to convert the source singer's voice to sound like that of the target singer, without changing the lyrical content. So far, most of the voice conversion studies mainly focus only on the speech voice conversion that is different from singing voice conversion. We note that singing conveys both lexical and emotional information through words and tones. It is one of the most expressive components in music and a means of entertainment as well as self expression. In this paper, we propose a novel singing voice conversion framework, that is based on Generative Adversarial Networks (GANs). The proposed GAN-based conversion framework, that we call SINGAN, consists of two neural networks: a discriminator to distinguish natural and converted singing voice, and a generator to deceive the discriminator. With GAN, we minimize the differences of the distributions between the original target parameters and the generated singing parameters. To our best knowledge, this is the first framework that uses generative adversarial networks for singing voice conversion. In experiments, we show that the proposed method effectively converts singing voices and outperforms the baseline approach.","PeriodicalId":145222,"journal":{"name":"2019 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122698943","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: 28