共查询到20条相似文献,搜索用时 15 毫秒
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人的听觉系统除了可以感受声音的强度、音调、音色和空间外,还可以在嘈杂的环境中感知和定位自己感兴趣的声音,计算听觉场景的算法尝试用计算机技术来模拟人的这种听觉处理功能,介绍人的听觉生理规律,对听觉场景分析的算法进行了有益的探索与研究。 相似文献
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Lei Yang Nanning Zheng Mei Chen Yang Yang Jie Yang 《International Journal of Computer Vision》2013,105(1):1-18
Recently, various bag-of-features (BoF) methods show their good resistance to within-class variations and occlusions in object categorization. In this paper, we present a novel approach for multi-object categorization within the BoF framework. The approach addresses two issues in BoF related methods simultaneously: how to avoid scene modeling and how to predict labels of an image when multiple categories of objects are co-existing. We employ a biased sampling strategy which combines the bottom-up, biologically inspired saliency information and loose, top-down class prior information for object class modeling. Then this biased sampling component is further integrated with a multi-instance multi-label leaning and classification algorithm. With the proposed biased sampling strategy, we can perform multi-object categorization within an image without semantic segmentation. The experimental results on PASCAL VOC2007 and SUN09 show that the proposed method significantly improves the discriminative ability of BoF methods and achieves good performance in multi-object categorization tasks. 相似文献
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人耳听觉系统能够从嘈杂的环境中筛选出自己感兴趣的语音,基于计算听觉场景分析的方法,论文采用倒谱法提取语音基音周期轨迹,以连续的基音周期轨迹为线索,按基音频率的整数倍提取各次谐波的频谱,再通过傅里叶逆变换重构分离后的语音。实验表明,在几种典型噪音环境下,该方法能有效将目标语音从背景噪声中分离,信噪比(SNR)和评价意见分(MOS)得到一定的提升,平均增益分别为5.67dB和0.36。 相似文献
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高维数据集合的最近邻查询性能会受到"维数灾难"(curse of dimensionality)现象的影响。提出了一种基于联合聚类的HC2(hypercube on co-clustering)高维索引结构。首先通过联合聚类算法同时降低数据尺寸和维数,将高维数据集合聚成若干较低维数的类,然后采用超立方体结构对每个类进行空间区域描述。在基于"过滤-精炼"(filter and refine)的查询过程中,计算查询点与各个类之间的距离下界,实现对聚类的有效过滤。为了提高距离下界对真实距离的逼近能力,采用了一种基于统计优化的超立方体区域描述方法SOHC2(statistically optimized hypercube on co-clustering),能够更加有效地缩小搜索空间,提高查询性能。理论分析和实验结果都表明,SOHC2的查询性能明显优于其他索引方法,适合大规模高维数据的查询;与同类索引结构相比,查询速度能够提高3倍以上。 相似文献
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将单人语音从双人混合语音中完整地分离出来是一个具有挑战性的问题,当两个语音信号的能量差别较大时,若两个语音的谐波在频率上存在重合部分,直接利用反傅里叶变换法对分离后的语音进行重构,会因频率掩蔽效应对重构语音的音色造成极大的影响,出现窜音现象.论文尝试找到傅里叶变换后的双人语音中对应的单人的基音频率及其谐波,处理后进行傅里叶逆变换重构,得到分离后的单人语音信号.实验表明,该方法所得的语音具有较好的音质,同时能有效地消除窜音现象. 相似文献
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Applied Intelligence - Multi-view learning is an attractive area of research where data is represented using multiple views, each containing some useful information. Many multi-view learning... 相似文献
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Monaural Speech Separation Based on Computational Auditory Scene Analysis and Objective Quality Assessment of Speech 总被引:1,自引:0,他引:1
《IEEE transactions on audio, speech, and language processing》2006,14(6):2014-2023
Monaural speech separation is a very challenging problem in speech signal processing. It has been studied extensively, and many separation systems based on computational auditory scene analysis (CASA) have been proposed in the last two decades. Although the research on CASA has tended to introduce high-level knowledge into separation processes using primitive data-driven methods, the knowledge on speech quality still has not been combined with it. This makes the performance evaluation of CASA mainly focused on the signal-to-noise ratio (SNR) improvement. Actually, the quality of the separated speech is not directly related to its SNR. In order to solve this problem, we propose a new method which combines CASA with objective quality assessment of speech (OQAS). In the grouping process of CASA, we use OQAS as the guide to instruct the CASA system. With this combination, the performance of the speech separation can be improved not only in SNR, but also in mean opinion score (MOS). Our system is systematically evaluated and compared with previous systems, and it yields substantially better performance, especially for the subjective perceptual quality of separated speech. 相似文献
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针对传统协同过滤推荐(collaborative filtering recommendation,CFR)受数据聚类预处理,评分矩阵稀疏性影响较大和多个评分矩阵之间不能知识迁移的问题,提出了一种基于联合聚类和评分矩阵共享的协同过滤推荐方法,以提高推荐系统精度和泛化能力。该方法首先通过联合聚类对原始评分矩阵进行用户和项目两个维度的聚类;然后对评分矩阵进行分解并取得共享组级评分矩阵;最后利用共享组级评分矩阵和迁移学习方法进行评分预测。对MovieLents和Book-Crossing两个数据集进行了仿真实验,结果表明该方法相比传统方法平均绝对误差减少近8%,有效地提高了协同过滤推荐的预测精度,为协同过滤推荐的应用提供借鉴。 相似文献
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Text categorization is the task of assigning predefined categories to natural language text. With the widely used 'bag of words' representation, previous researches usually assign a word with values such that whether this word appears in the document concerned or how frequently this word appears. Although these values are useful for text categorization, they have not fully expressed the abundant information contained in the document. This paper explores the effect of other types of values, which express the distribution of a word in the document. These novel values assigned to a word are called distributional features, which include the compactness of the appearances of the word and the position of the first appearance of the word. The proposed distributional features are exploited by a tf idf style equation and different features are combined using ensemble learning techniques. Experiments show that the distributional features are useful for text categorization. In contrast to using the traditional term frequency values solely, including the distributional features requires only a little additional cost, while the categorization performance can be significantly improved. Further analysis shows that the distributional features are especially useful when documents are long and the writing style is casual. 相似文献
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Ashish Kapoor Kristen Grauman Raquel Urtasun Trevor Darrell 《International Journal of Computer Vision》2010,88(2):169-188
Discriminative methods for visual object category recognition are typically non-probabilistic, predicting class labels but
not directly providing an estimate of uncertainty. Gaussian Processes (GPs) provide a framework for deriving regression techniques
with explicit uncertainty models; we show here how Gaussian Processes with covariance functions defined based on a Pyramid
Match Kernel (PMK) can be used for probabilistic object category recognition. Our probabilistic formulation provides a principled
way to learn hyperparameters, which we utilize to learn an optimal combination of multiple covariance functions. It also offers
confidence estimates at test points, and naturally allows for an active learning paradigm in which points are optimally selected
for interactive labeling. We show that with an appropriate combination of kernels a significant boost in classification performance
is possible. Further, our experiments indicate the utility of active learning with probabilistic predictive models, especially
when the amount of training data labels that may be sought for a category is ultimately very small. 相似文献
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信息论联合聚类算法及其在视频镜头聚类中的应用 总被引:2,自引:0,他引:2
视频镜头自动聚类是基于内容索引与检索领域中的重要研究课题.以往相关工作,缺乏考虑描述镜头内容的特征与特征间存在关联性以及关联特征对镜头相似性度量和镜头聚类性能带来的影响.为提供更合理的镜头相似性度量,该文基于信息论联合聚类算法,将特征关联性挖掘和镜头聚类描述为彼此依附的同步优化过程.同时,为自动估计视频中镜头类别数,文中还提出基于贝叶斯信息准则的类别数估计算法. 相似文献
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Alexander Binder Klaus-Robert Müller Motoaki Kawanabe 《International Journal of Computer Vision》2012,99(3):281-301
We study the problem of classifying images into a given, pre-determined taxonomy. This task can be elegantly translated into the structured learning framework. However, despite its power, structured learning has known limits in scalability due to its high memory requirements and slow training process. We propose an efficient approximation of the structured learning approach by an ensemble of local support vector machines (SVMs) that can be trained efficiently with standard techniques. A?first theoretical discussion and experiments on toy-data allow to shed light onto why taxonomy-based classification can outperform taxonomy-free approaches and why an appropriately combined ensemble of local SVMs might be of high practical use. Further empirical results on subsets of Caltech256 and VOC2006 data indeed show that our local SVM formulation can effectively exploit the taxonomy structure and thus outperforms standard multi-class classification algorithms while it achieves on par results with taxonomy-based structured algorithms at a significantly decreased computing time. 相似文献
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文章以比较购物搜索中的商品数据自动分类为应用背景,探讨短文本数据的分类问题,比较了常用的文本分类算法的特点,在此基础上提出k-NN与NB相结合的多分类器方案,对于NB算法分类不可信的情况下改用k-NN算法进行再次分类,并充分利用NB的中间结果供k-NN剪枝时作参考.实验数据表明该方法在与NB相近的时间复杂度下可明显地提高短文本分类的正确率和召回率,达到实际应用的要求. 相似文献