Instrument Patch with presets：直接在Channel Strip的I／O下选择乐器插件音色，这是已经配置了音色设置以及相应效果和均衡插件的。用户可以直接使用，也可以对其中的参数做些调整，或者在此基础上重新配置均衡与效果器。这是一种非...Instrument Patch with presets：直接在Channel Strip的I／O下选择乐器插件音色，这是已经配置了音色设置以及相应效果和均衡插件的。用户可以直接使用，也可以对其中的参数做些调整，或者在此基础上重新配置均衡与效果器。这是一种非常好的手段，可以让用户只关心最终的音色，而不用去研究哪个乐器插件中有需要的音色，这个音色应该配置哪些效果器、如何处理它的均衡等等。实际上，任何一个用户关心的只是最终音色的表现，至于使用了哪个乐器插件实际是其次的问题，甚至是可以被忽略的。展开更多
With the rapid development of the Internet, multi documents summarization is becoming a very hot research topic. In order to generate a summarization that can effectively characterize the original information from doc...With the rapid development of the Internet, multi documents summarization is becoming a very hot research topic. In order to generate a summarization that can effectively characterize the original information from documents, this paper proposes a multi documents summarization approach based on the physical features and logical structure of the document set. This method firstly clusterssimilar sentences into several Logical Topics (LTs), and then orders these topics according to their physical features of multi documents. After that, sentences used for the summarization are extracted from these LTs, and finally the summarization is generated via certain sorting algorithms. Our experiments show that the information coverage rate of our method is 8.83% higher than those methods based solely on logical structures, and 14.31% higher than Top-N method.展开更多
文摘Instrument Patch with presets：直接在Channel Strip的I／O下选择乐器插件音色，这是已经配置了音色设置以及相应效果和均衡插件的。用户可以直接使用，也可以对其中的参数做些调整，或者在此基础上重新配置均衡与效果器。这是一种非常好的手段，可以让用户只关心最终的音色，而不用去研究哪个乐器插件中有需要的音色，这个音色应该配置哪些效果器、如何处理它的均衡等等。实际上，任何一个用户关心的只是最终音色的表现，至于使用了哪个乐器插件实际是其次的问题，甚至是可以被忽略的。
文摘With the rapid development of the Internet, multi documents summarization is becoming a very hot research topic. In order to generate a summarization that can effectively characterize the original information from documents, this paper proposes a multi documents summarization approach based on the physical features and logical structure of the document set. This method firstly clusterssimilar sentences into several Logical Topics (LTs), and then orders these topics according to their physical features of multi documents. After that, sentences used for the summarization are extracted from these LTs, and finally the summarization is generated via certain sorting algorithms. Our experiments show that the information coverage rate of our method is 8.83% higher than those methods based solely on logical structures, and 14.31% higher than Top-N method.
