随着大数据应用日益增多,NoSQL(not only structured query language)数据库管理系统得到了快速发展。如何对NoSQL数据库和关系型数据库进行有效集成成为研究热点之一。在现有研究成果中,在这些异构的多数据库之间进行联接(join)操作时...随着大数据应用日益增多,NoSQL(not only structured query language)数据库管理系统得到了快速发展。如何对NoSQL数据库和关系型数据库进行有效集成成为研究热点之一。在现有研究成果中,在这些异构的多数据库之间进行联接(join)操作时,所采用的分页查询方法产生的延迟较大。针对这一问题,提出了一种分页预取的模型,并重点研究了其基本构成、预取方式以及运行机制。基于该模型,设计开发了原型系统,对其效果进行了验证,达到了预期目标。与无预取的分页查询方法相比,所建模型可以减少分页查询延迟,从而提升跨数据库联接操作的效率。展开更多
To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for...To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.展开更多
文摘随着大数据应用日益增多,NoSQL(not only structured query language)数据库管理系统得到了快速发展。如何对NoSQL数据库和关系型数据库进行有效集成成为研究热点之一。在现有研究成果中,在这些异构的多数据库之间进行联接(join)操作时,所采用的分页查询方法产生的延迟较大。针对这一问题,提出了一种分页预取的模型,并重点研究了其基本构成、预取方式以及运行机制。基于该模型,设计开发了原型系统,对其效果进行了验证,达到了预期目标。与无预取的分页查询方法相比,所建模型可以减少分页查询延迟,从而提升跨数据库联接操作的效率。
基金The National Natural Science Foundation of China(No.61273266,61231002,61301219,61375028)the Specialized Research Fund for the Doctoral Program of Higher Education(No.20110092130004)the Natural Science Foundation of Shandong Province(No.ZR2014FQ016)
文摘To solve the problem of mismatching features in an experimental database, which is a key technique in the field of cross-corpus speech emotion recognition, an auditory attention model based on Chirplet is proposed for feature extraction.First, in order to extract the spectra features, the auditory attention model is employed for variational emotion features detection. Then, the selective attention mechanism model is proposed to extract the salient gist features which showtheir relation to the expected performance in cross-corpus testing.Furthermore, the Chirplet time-frequency atoms are introduced to the model. By forming a complete atom database, the Chirplet can improve the spectrum feature extraction including the amount of information. Samples from multiple databases have the characteristics of multiple components. Hereby, the Chirplet expands the scale of the feature vector in the timefrequency domain. Experimental results show that, compared to the traditional feature model, the proposed feature extraction approach with the prototypical classifier has significant improvement in cross-corpus speech recognition. In addition, the proposed method has better robustness to the inconsistent sources of the training set and the testing set.
