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Calculator

于 2014-11-24 发布文件大小:605KB

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下载积分: 1 下载次数: 2

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代码说明：

Project of calculator program as an example for students for studying of C++ Builder programming environment.

文件列表：

DioCalc
.......\PKalc.bpr,4272,2013-04-03
.......\PKalc.cpp,1069,2009-09-20
.......\PKalc.exe,194048,2013-12-24
.......\PKalc.obj,18438,2013-12-24
.......\PKalc.res,1260,2013-04-03
.......\PKalc.tds,2162688,2013-12-24
.......\PKalc.~bpr,4198,2009-09-20
.......\SEdit.h,515,2009-09-20
.......\UKalc.cpp,3593,2013-04-03
.......\UKalc.ddp,51,2013-04-03
.......\UKalc.dfm,169610,2013-04-03
.......\UKalc.h,1964,2013-04-03
.......\UKalc.obj,91433,2013-12-24
.......\UKalc.~cpp,3593,2013-04-03
.......\UKalc.~ddp,51,2013-04-03
.......\UKalc.~dfm,169610,2013-04-03
.......\UKalc.~h,1964,2013-04-03

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recognition
本实验语音库为免费的柏林情感语音库，其采样频率为16KHZ，16bit量化。该语音库共有500 句情感语音信号，分别由十名专业演员(5 男，5 女)在不同情感状态下（高兴、愤怒、平静、悲伤、害怕、厌烦、憎恨）朗读十句不同文本的德语组成。本实验选取其中的部分情感（高兴、愤怒、悲伤）加以识别。仿真实验环境为MATLAB7.0。实验选取的情感特征为短时平均能量、短时平均幅度、基频和短时过零率。为了降低不同人在表达不同情感时的个人差异造成的影响，本文实验过程中将提取的情感特征进行归一化处理。归一化采取将同一个人的三种情感语音信号的情感特征放在一起归一化处理，并将归一化后的情感特征作为KNN分类器的训练样本和测试样本。该实验使用了传统的KNN算法和改进算法两种识别方式，通过实验结果对比了各自的特点。 (This experiment YuYinKu for free Berlin YuYinKu emotion, the sampling frequency for 16 KHZ, 16 bit quantification. The YuYinKu 500 words of emotional speech signal, respectively by ten professional actors (5 male, five female) in different emotional state (happy, anger, sadness, fear, calm, bored, hate) read ten sentences in different text of the German. This experiment selection of part of the emotions (joy, anger, sadness) is recognized. The simulation experiment environment for MATLAB7.0. The selection of emotional characteristics for short-term average energy, short-term average amplitude, frequency, and the short zero rate. In order to reduce the different people in different emotional expression of the impact of individual differences, the experiment process will extract emotional characteristics is normalized. The normalized will take the same three kind of emotional speech signal emotional features normalized put together, and after the normalized the emotional characteristics)

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CUDA by Example, written by two senior members of the CUDA software platform team, shows programmers how to employ this new technology, including a concise introduction to the CUDA platform and architecture, as well as a quick-start guide to CUDA C.

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pvvnn
一种流形学习算法（很好用），包括主成分分析、因子分析、贝叶斯分析，包含收发两个客户端的链路级通信程序。( A fluid manifold learning algorithm (good use), Including principal component analysis, factor analysis, Bayesian analysis, Contains two clients receive link-level communications program.)

2017-04-15 21:53:49下载

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