Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific最新文献_第5页

Selection of best match keyword using spoken term detection for spoken document indexing 使用口语词检测为口语文档索引选择最佳匹配关键字

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041589

Kentaro Domoto, T. Utsuro, N. Sawada, H. Nishizaki

引用次数: 0

Self-learning-based signal decomposition for multimedia applications: A review and comparative study 基于自学习的多媒体信号分解:综述与比较研究

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041778

Li-Wei Kang, C. Yeh, Duan-Yu Chen, Chia-Tsung Lin

{"title":"Self-learning-based signal decomposition for multimedia applications: A review and comparative study","authors":"Li-Wei Kang, C. Yeh, Duan-Yu Chen, Chia-Tsung Lin","doi":"10.1109/APSIPA.2014.7041778","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041778","url":null,"abstract":"Decomposition of a signal (e.g., image or video) into multiple semantic components has been an effective research topic for various image/video processing applications, such as image/video denoising, enhancement, and inpainting. In this paper, we present a survey of signal decomposition frameworks based on the uses of sparsity and morphological diversity in signal mixtures and its applications in multimedia. First, we analyze existing MCA (morphological component analysis) based image decomposition frameworks with their applications and explore the potential limitations of these approaches for image denoising. Then, we discuss our recently proposed self-learning based image decomposition framework with its applications to several image/video denoising tasks, including single image rain streak removal, denoising, deblocking, joint super-resolution and deblocking for a highly compressed image/video. By advancing sparse representation and morphological diversity of image signals, the proposed framework first learns an over-complete dictionary from the high frequency part of an input image for reconstruction purposes. An unsupervised or supervised clustering technique is applied to the dictionary atoms for identifying the morphological component corresponding to the noise pattern of interest (e.g., rain streaks, blocking artifacts, or Gaussian noises). Different from prior learning-based approaches, our method does not need to collect training data in advance and no image priors are required. Our experimental results have confirmed the effectiveness and robustness of the proposed framework, which has been shown to outperform state-of-the-art approaches.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116767970","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: 8

R-cube: A dialogue agent for restaurant recommendation and reservation R-cube:一个餐厅推荐和预订的对话代理

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041732

Seokhwan Kim, Rafael E. Banchs

引用次数: 18

Real-time depth map generation using hybrid multi-view cameras 实时深度图生成使用混合多视图相机

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041683

Yunseok Song, Dong-Won Shin, Eunsang Ko, Yo-Sung Ho

引用次数: 2

Robust anchorperson detection based on audio streams using a hybrid I-vector and DNN system 基于i向量和DNN混合系统的音频流鲁棒主播检测

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041717

Yun-Fan Chang, Payton Lin, Shao-Hua Cheng, Kai-Hsuan Chan, Y. Zeng, Chia-Wei Liao, Wen-Tsung Chang, Y. Wang, Yu Tsao

{"title":"Robust anchorperson detection based on audio streams using a hybrid I-vector and DNN system","authors":"Yun-Fan Chang, Payton Lin, Shao-Hua Cheng, Kai-Hsuan Chan, Y. Zeng, Chia-Wei Liao, Wen-Tsung Chang, Y. Wang, Yu Tsao","doi":"10.1109/APSIPA.2014.7041717","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041717","url":null,"abstract":"Anchorperson segment detection enables efficient video content indexing for information retrieval. Anchorperson detection based on audio analysis has gained popularity due to lower computational complexity and satisfactory performance. This paper presents a robust framework using a hybrid I-vector and deep neural network (DNN) system to perform anchorperson detection based on audio streams of video content. The proposed system first applies I-vector to extract speaker identity features from the audio data. With the extracted speaker identity features, a DNN classifier is then used to verify the claimed anchorperson identity. In addition, subspace feature normalization (SFN) is incorporated into the hybrid system for robust feature extraction to compensate the audio mismatch issues caused by recording devices. An anchorperson verification experiment was conducted to evaluate the equal error rate (EER) of the proposed hybrid system. Experimental results demonstrate that the proposed system outperforms the state-of-the-art hybrid I-vector and support vector machine (SVM) system. Moreover, the proposed system was further enhanced by integrating SFN to effectively compensate the audio mismatch issues in anchorperson detection tasks.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131540250","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

Comparison the training methods of neural network for English and Thai character recognition 神经网络训练方法在英文和泰文字符识别中的比较

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041795

A. Saenthon, Natchanon Sukkhadamrongrak

引用次数: 1

Redefining self-similarity in natural images for denoising using graph signal gradient 重新定义自然图像的自相似度，利用图信号梯度去噪

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041627

Jiahao Pang, Gene Cheung, Wei Hu, O. Au

{"title":"Redefining self-similarity in natural images for denoising using graph signal gradient","authors":"Jiahao Pang, Gene Cheung, Wei Hu, O. Au","doi":"10.1109/APSIPA.2014.7041627","DOIUrl":"https://doi.org/10.1109/APSIPA.2014.7041627","url":null,"abstract":"Image denoising is the most basic inverse imaging problem. As an under-determined problem, appropriate definition of image priors to regularize the problem is crucial. Among recent proposed priors for image denoising are: i) graph Laplacian regularizer where a given pixel patch is assumed to be smooth in the graph-signal domain; and ii) self-similarity prior where image patches are assumed to recur throughout a natural image in non-local spatial regions. In our first contribution, we demonstrate that the graph Laplacian regularizer converges to a continuous time functional counterpart, and careful selection of its features can lead to a discriminant signal prior. In our second contribution, we redefine patch self-similarity in terms of patch gradients and argue that the new definition results in a more accurate estimate of the graph Laplacian matrix, and thus better image denoising performance. Experiments show that our designed algorithm based on graph Laplacian regularizer and gradient-based self-similarity can outperform non-local means (NLM) denoising by up to 1.4 dB in PSNR.","PeriodicalId":231382,"journal":{"name":"Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114117807","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: 29

Recursive neural network paraphrase identification for example-based dialog retrieval 基于实例的对话检索递归神经网络释义识别

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041777

Lasguido Nio, S. Sakti, Graham Neubig, T. Toda, Satoshi Nakamura

引用次数: 0

Multi-agent ad hoc team partitioning by observing and modeling single-agent performance 通过观察和建模单智能体性能来进行多智能体特别团队划分

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041644

Etkin Baris Ozgul, Somchaya Liemhetcharat, K. H. Low

引用次数: 1

Spectral-temporal receptive fields and MFCC balanced feature extraction for noisy speech recognition 噪声语音识别的频谱-时间接受野和MFCC平衡特征提取

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific Pub Date : 2014-12-01 DOI: 10.1109/APSIPA.2014.7041624

Jia-Ching Wang, Chang-Hong Lin, En-Ting Chen, P. Chang

引用次数: 4