Abstract
This paper studies the use of three different approaches to reduce the dimensionality of a type of spectral–temporal features, called motion picture expert group (MPEG)-7 audio signature descriptors (ASD). The studied approaches include principal component analysis (PCA), independent component analysis (ICA), and factor analysis (FA). These approaches are applied to ASD features obtained from audio items with or without distortion. These low-dimensional features are used as queries to a dataset containing low-dimensional features extracted from undistorted items. Doing so, we may investigate the distortion-resistant capability of each approach. The experimental results show that features obtained by the ICA or FA reduction approaches have higher identification accuracy than the PCA approach for moderately distorted items. Therefore, to extract features from distorted items, ICA or FA approaches should also be considered in addition to the PCA approach.
Highlights
It is very useful for many applications to reduce the dimensionality of data for higher processing speed, if the dimension-reduced data, called features, can faithfully preserve important information from the original data
We have shown that a variation of 2-D principal component analysis (PCA) is slightly better
The results show that the PCA approach has a slight advantage if the test item is distortionless or only lightly distorted (192 k MP3)
Summary
It is very useful for many applications to reduce the dimensionality of data for higher processing speed, if the dimension-reduced data, called features, can faithfully preserve important information from the original data. Doing so can significantly reduce the recognition time as well as storage space Another application of performing dimensionality reduction is to increase the processing speed. We have studied some dimensionality-reduction methods for MPEG-7 ASD, such as PCA (principal component analysis) [2,3]. In these papers, we use PCA, but not ICA or FA, to decompose the feature matrix, and keep large components and discard smaller ones. The experiments apply reduction techniques only to the MPEG-7 ASD, the presented approaches, are general approaches and can be applied to other types of features and application scenarios.
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