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Description: An image-based classification application satisfying all the aims above has been implemented. It
allows for flexible evaluation of SVM classification of facial expressions for arbitrary user-defined
emotion categories and various SVM settings.
Platform: |
Size: 982016 |
Author: oskard858624 |
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Description: SPEECH-BASED EMOTION CLASSIFICATION USING MULTICLASS SVM WITH HYBRID
KERNEL AND THRESHOLDING FUSION-SPEECH-BASED EMOTION CLASSIFICATION USING MULTICLASS SVM WITH HYBRID KERNEL AND THRESHOLDING FUSION
Platform: |
Size: 21504 |
Author: 柴羊 |
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Description: 针对稀疏表示识别方法需要大量样本训练过完备字典且特征冗余度较高的问题，提出了结合过完备字典学习与PCA降维的小样本语音情感识别算法.该方法首先用PCA降维方法将特征降维，再将处理后的特征用于过完备字典训练与稀疏表示识别方法，从而给出了语音情感特征的稀疏表示方法，并确定了新算法的具体步骤.为验证其有效性，在同等特征维数下，将方法与BP, SVM进行比较，并对比、分析语音情感特征稀疏化前后对语音情感识别率、时间效率以及空间效率的影响.试验结果表明，所提出方法的识别率比SVM与BP高 与采用稀疏化前的特征相比，稀疏化后的特征向量更便于处理，平均识别率提高约15 ，时间效率提高近原来的1 /2，空间效率提升近原来的1 /3.
-Identification methods for sparse representation requires a lot of training samples and high over-complete dictionary feature redundancy problem, a combination of over-complete dictionary learning and PCA dimension small sample speech emotion recognition algorithms. Firstly, the PCA dimension reduction methods feature reduction, feature and then treatment for the over-complete dictionary training and recognition sparse representation, which gives a speech emotion feature sparse representation, and to determine the specific steps of the new algorithm. To verify its validity, in Under the same number of features, the method and BP, SVM compare and contrast, analyze the impact before and after the speech emotion feature sparse speech emotion recognition rate, time-efficient and space-efficient. experimental results show that the recognition rate of the proposed method than High SVM and BP compared to pre-thinning characteristics using eigenvectors easier after thinning processing, the av
Platform: |
Size: 629760 |
Author: wangming |
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Description: Human Emotion Recognition using modified Gabor as a feture extraction and SVM classifier
Platform: |
Size: 1801216 |
Author: vassi22 |
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Description: ORL标准人脸库，40个人每人10张不同角度不同表情照片。
通过PCA主成分分析提取特征，再用SVM进行分类，达到较好的人脸识别。-Base on the standard facial library ORL,which contend 40 persons ,10 photos per one in different ankle and different emotion. The program use PCA and SVM method to recognize face.
Platform: |
Size: 9882624 |
Author: ChelseaLeung |
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Description: 情感分析的源码，需要libsvm包，导入项目然后写个主类调用就可以(Emotion analysis of the source code, you need libsvm package)
Platform: |
Size: 40960 |
Author: 李雷
|
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Description: 语音情感识别分类，在中科大录制的语音情感数据库CASIA中来实现的(Speech emotion recognition and classification is implemented in CASIA, a speech emotion database recorded by China University of science and technology.)
Platform: |
Size: 1024 |
Author: chestnut_ |
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Description: 基于SVM的语音情感识别，可以从用户的话语中识别愤怒、开心、快乐、悲伤等情感(SVM-based speech emotion recognition, which can identify emotions such as anger, happiness, happiness, sadness, etc. from the user's words.)
Platform: |
Size: 33012736 |
Author: Last minute |
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Description: 基于svm的情感识别系统，有gui界面，特征提取是用mfcc。(The emotion recognition system based on SVM has GUI interface and MFCC is used for feature extraction.)
Platform: |
Size: 3769344 |
Author: 发改局 |
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