尽管音视频编码标准(Audio and Video Coding Standdard,AVS)的编码性能可以与H.264相媲美,但是H.264的应用范围更加广泛,因此视频由AVS标准转码成H.264标准具有很大的应用前景。目前,主流的转码方法是将AVS的分块模式与H.264的分块模...尽管音视频编码标准(Audio and Video Coding Standdard,AVS)的编码性能可以与H.264相媲美,但是H.264的应用范围更加广泛,因此视频由AVS标准转码成H.264标准具有很大的应用前景。目前,主流的转码方法是将AVS的分块模式与H.264的分块模式映射的方式降低转码复杂度,但是技术之间的差异导致这两种标准之间的分块模式并不是一一映射的关系,因此会导致编码效率大幅度降低。提出一种基于改进KNN(K最邻近节点)算法的AVS到H.264/AVC快速转码方法。充分利用了AVS码流中的各种信息,通过改进的KNN算法建立了中间信息和H.264分块模式之间的映射模型。根据AVS中运动矢量信息的差异自适应确定H.264可能的分块模式,实验结果表明上述问题得到有效解决,该算法在保证H.264编码效率的前提下大幅降低了转码复杂度。展开更多
The interaction between humans and machines has become an issue of concern in recent years.Besides facial expressions or gestures,speech has been evidenced as one of the foremost promising modalities for automatic emo...The interaction between humans and machines has become an issue of concern in recent years.Besides facial expressions or gestures,speech has been evidenced as one of the foremost promising modalities for automatic emotion recognition.Effective computing means to support HCI(Human-Computer Interaction)at a psychological level,allowing PCs to adjust their reactions as per human requirements.Therefore,the recognition of emotion is pivotal in High-level interactions.Each Emotion has distinctive properties that form us to recognize them.The acoustic signal produced for identical expression or sentence changes is essentially a direct result of biophysical changes,(for example,the stress instigated narrowing of the larynx)set off by emotions.This connection between acoustic cues and emotions made Speech Emotion Recognition one of the moving subjects of the emotive computing area.The most motivation behind a Speech Emotion Recognition algorithm is to observe the emotional condition of a speaker from recorded Speech signals.The results from the application of k-NN and OVA-SVM for MFCC features without and with a feature selection approach are presented in this research.The MFCC features from the audio signal were initially extracted to characterize the properties of emotional speech.Secondly,nine basic statistical measures were calculated from MFCC and 117-dimensional features were consequently obtained to train the classifiers for seven different classes(Anger,Happiness,Disgust,Fear,Sadness,Disgust,Boredom and Neutral)of emotions.Next,Classification was done in four steps.First,all the 117-features are classified using both classifiers.Second,the best classifier was found and then features were scaled to[-1,1]and classified.In the third step,the with or without feature scaling which gives better performance was derived from the results of the second step and the classification was done for each of the basic statistical measures separately.Finally,in the fourth step,the combination of statistical measures which gives better performance was derived using the forward feature selection method Experiments were carried out using k-NN with different k values and a linear OVA-based SVM classifier with different optimal values.Berlin emotional speech database for the German language was utilized for testing the planned methodology and recognition rates as high as 60%accomplished for the recognition of emotion from voice signal for the set of statistical measures(median,maximum,mean,Inter-quartile range,skewness).OVA-SVM performs better than k-NN and the use of the feature selection technique gives a high rate.展开更多
针对密度峰值聚类算法(clustering by fast search and find of density peaks,DPC)聚类无特定形状的实际数据集时聚类精度欠佳的问题,提出一种最优化密度估计的密度峰聚值类算法。使用最优Oracle逼近(Oracle approximating shrinkage,...针对密度峰值聚类算法(clustering by fast search and find of density peaks,DPC)聚类无特定形状的实际数据集时聚类精度欠佳的问题,提出一种最优化密度估计的密度峰聚值类算法。使用最优Oracle逼近(Oracle approximating shrinkage,AS)计算出最优协方差矩阵,利用最优协方差矩阵构造马氏距离,通过最优协方差矩阵提高DPC对数据相似度的区分能力,在此基础上结合K近邻算法,实现数据样本密度最优估计,利用最优密度估计提高DPC对实际数据集的聚类精度。在人工数据集和UCI真实数据集上进行仿真实验,实验结果表明,改进DPC算法的思路是可行的。展开更多
文摘尽管音视频编码标准(Audio and Video Coding Standdard,AVS)的编码性能可以与H.264相媲美,但是H.264的应用范围更加广泛,因此视频由AVS标准转码成H.264标准具有很大的应用前景。目前,主流的转码方法是将AVS的分块模式与H.264的分块模式映射的方式降低转码复杂度,但是技术之间的差异导致这两种标准之间的分块模式并不是一一映射的关系,因此会导致编码效率大幅度降低。提出一种基于改进KNN(K最邻近节点)算法的AVS到H.264/AVC快速转码方法。充分利用了AVS码流中的各种信息,通过改进的KNN算法建立了中间信息和H.264分块模式之间的映射模型。根据AVS中运动矢量信息的差异自适应确定H.264可能的分块模式,实验结果表明上述问题得到有效解决,该算法在保证H.264编码效率的前提下大幅降低了转码复杂度。
文摘The interaction between humans and machines has become an issue of concern in recent years.Besides facial expressions or gestures,speech has been evidenced as one of the foremost promising modalities for automatic emotion recognition.Effective computing means to support HCI(Human-Computer Interaction)at a psychological level,allowing PCs to adjust their reactions as per human requirements.Therefore,the recognition of emotion is pivotal in High-level interactions.Each Emotion has distinctive properties that form us to recognize them.The acoustic signal produced for identical expression or sentence changes is essentially a direct result of biophysical changes,(for example,the stress instigated narrowing of the larynx)set off by emotions.This connection between acoustic cues and emotions made Speech Emotion Recognition one of the moving subjects of the emotive computing area.The most motivation behind a Speech Emotion Recognition algorithm is to observe the emotional condition of a speaker from recorded Speech signals.The results from the application of k-NN and OVA-SVM for MFCC features without and with a feature selection approach are presented in this research.The MFCC features from the audio signal were initially extracted to characterize the properties of emotional speech.Secondly,nine basic statistical measures were calculated from MFCC and 117-dimensional features were consequently obtained to train the classifiers for seven different classes(Anger,Happiness,Disgust,Fear,Sadness,Disgust,Boredom and Neutral)of emotions.Next,Classification was done in four steps.First,all the 117-features are classified using both classifiers.Second,the best classifier was found and then features were scaled to[-1,1]and classified.In the third step,the with or without feature scaling which gives better performance was derived from the results of the second step and the classification was done for each of the basic statistical measures separately.Finally,in the fourth step,the combination of statistical measures which gives better performance was derived using the forward feature selection method Experiments were carried out using k-NN with different k values and a linear OVA-based SVM classifier with different optimal values.Berlin emotional speech database for the German language was utilized for testing the planned methodology and recognition rates as high as 60%accomplished for the recognition of emotion from voice signal for the set of statistical measures(median,maximum,mean,Inter-quartile range,skewness).OVA-SVM performs better than k-NN and the use of the feature selection technique gives a high rate.
文摘针对密度峰值聚类算法(clustering by fast search and find of density peaks,DPC)聚类无特定形状的实际数据集时聚类精度欠佳的问题,提出一种最优化密度估计的密度峰聚值类算法。使用最优Oracle逼近(Oracle approximating shrinkage,AS)计算出最优协方差矩阵,利用最优协方差矩阵构造马氏距离,通过最优协方差矩阵提高DPC对数据相似度的区分能力,在此基础上结合K近邻算法,实现数据样本密度最优估计,利用最优密度估计提高DPC对实际数据集的聚类精度。在人工数据集和UCI真实数据集上进行仿真实验,实验结果表明,改进DPC算法的思路是可行的。
