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
The pressure and horizontal particle velocity combined descriptions in the very low frequency acoustic field of shallow wa- ter integrated with the concept of effective depth of Pekeris wave- guide is proposed, especi...The pressure and horizontal particle velocity combined descriptions in the very low frequency acoustic field of shallow wa- ter integrated with the concept of effective depth of Pekeris wave- guide is proposed, especially the active component of the pressure and horizontal particle velocity cross-spectrum, also called ho- rizontal complex cross acoustic intensity, when only two normal modes are trapped in the waveguide. Both the approximate theo- retic analysis and the numerical results show that the sign of the horizontal complex cross acoustic intensity active component is independent of the range when vertically deployed receiving dual sensors are placed in appropriate depths, the sum of which is equal to the waveguide effective depth, so it can be used to tell whether the sound source is near the surface or underwater; while the range rate is expected to be measured by utilizing the sign distribution characteristic of the reactive component. The further robustness analysis of the depth classification algorithm shows that the existence of shear waves in semi infinite basement and the change of acoustic velocity profiles have few effects on the application of this method, and the seabed attenuation will limit the detection range, but the algorithm still has a good robustness in the valid detection range.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supported by the National Natural Science Foundation of China(1140440611374072)
文摘The pressure and horizontal particle velocity combined descriptions in the very low frequency acoustic field of shallow wa- ter integrated with the concept of effective depth of Pekeris wave- guide is proposed, especially the active component of the pressure and horizontal particle velocity cross-spectrum, also called ho- rizontal complex cross acoustic intensity, when only two normal modes are trapped in the waveguide. Both the approximate theo- retic analysis and the numerical results show that the sign of the horizontal complex cross acoustic intensity active component is independent of the range when vertically deployed receiving dual sensors are placed in appropriate depths, the sum of which is equal to the waveguide effective depth, so it can be used to tell whether the sound source is near the surface or underwater; while the range rate is expected to be measured by utilizing the sign distribution characteristic of the reactive component. The further robustness analysis of the depth classification algorithm shows that the existence of shear waves in semi infinite basement and the change of acoustic velocity profiles have few effects on the application of this method, and the seabed attenuation will limit the detection range, but the algorithm still has a good robustness in the valid detection range.
