结合乐理理论和信号处理理论,针对传统和弦识别仅考虑音高特性的音级轮廓特征PCP(pitch class profile)造成正确识别率较低的问题,提出一种以反映听觉特性的MFCC(mel frequency cepstral coefficent)与PCP的联合特征和稀疏表示分类器(sp...结合乐理理论和信号处理理论,针对传统和弦识别仅考虑音高特性的音级轮廓特征PCP(pitch class profile)造成正确识别率较低的问题,提出一种以反映听觉特性的MFCC(mel frequency cepstral coefficent)与PCP的联合特征和稀疏表示分类器(sparse representation classification,SRC)的和弦识别方法.通过对两特征矢量的叠加构成新的和弦特征,然后利用SRC进行和弦识别.实验结果表明,与传统方法的识别率相比,本方法的识别率大幅提高.展开更多
Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typ...Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.展开更多
文摘结合乐理理论和信号处理理论,针对传统和弦识别仅考虑音高特性的音级轮廓特征PCP(pitch class profile)造成正确识别率较低的问题,提出一种以反映听觉特性的MFCC(mel frequency cepstral coefficent)与PCP的联合特征和稀疏表示分类器(sparse representation classification,SRC)的和弦识别方法.通过对两特征矢量的叠加构成新的和弦特征,然后利用SRC进行和弦识别.实验结果表明,与传统方法的识别率相比,本方法的识别率大幅提高.
基金Project(2019JJ40047)supported by the Hunan Provincial Natural Science Foundation of ChinaProject(kq2014057)supported by the Changsha Municipal Natural Science Foundation,China。
文摘Face recognition has been widely used and developed rapidly in recent years.The methods based on sparse representation have made great breakthroughs,and collaborative representation-based classification(CRC)is the typical representative.However,CRC cannot distinguish similar samples well,leading to a wrong classification easily.As an improved method based on CRC,the two-phase test sample sparse representation(TPTSSR)removes the samples that make little contribution to the representation of the testing sample.Nevertheless,only one removal is not sufficient,since some useless samples may still be retained,along with some useful samples maybe being removed randomly.In this work,a novel classifier,called discriminative sparse parameter(DSP)classifier with iterative removal,is proposed for face recognition.The proposed DSP classifier utilizes sparse parameter to measure the representation ability of training samples straight-forward.Moreover,to avoid some useful samples being removed randomly with only one removal,DSP classifier removes most uncorrelated samples gradually with iterations.Extensive experiments on different typical poses,expressions and noisy face datasets are conducted to assess the performance of the proposed DSP classifier.The experimental results demonstrate that DSP classifier achieves a better recognition rate than the well-known SRC,CRC,RRC,RCR,SRMVS,RFSR and TPTSSR classifiers for face recognition in various situations.
