Comparative study of methods for reducing dimensionality of MPEG-7 audio signature descriptors
We study how to reduce the dimensionality of the MPEG-7 audio signature descriptors in this paper. With the aid of the dimension-reduced descriptors, the comparison time for detecting copyrighted audio can be significantly reduced. The studied methods include block average, principal component analysis (PCA), Hadamard transform, Haar transform, and CDF (Cohen-Daubechies-Feauveau) 9/7 wavelet transform. For the latter four methods, we also examine whether different partition methods would affect the accuracy. The simulation results show that different reduction methods should use different partition strategies for best accuracy. In addition, we also compare the computational complexity of these methods. The experimental results show that, except the CDF 9/7 method, the rest four methods yield comparable accuracy for undistorted and MP-3 coded audio. When also considering the computational complexity, the block average method is a better choice.
mpeg-7 audio signature descriptor principal component analysis hadamard transform haar transform cdf 9/7 wavelet