Efficient Content Based Music Retrieval with Multiple Acoustic Feature Composition
In this paper, we present a new approach to constructing music descriptors to support efficient content-based music retrieval and classification. The system applies multiple musical properties combined with a hybrid architecture based on principal component analysis (PCA) and a multilayer perceptron neural network. This architecture enables straightforward incorporation of multiple musical feature vectors, based on properties such as timbral texture, pitch, and rhythm structure, into a single low-dimensioned vector that is more effective for classification than the larger individual feature vectors. The use of supervised training enables incorporation of human musical perception that further enhances the classification process. We compare our approach with state of the art techniques and demonstrate its effectiveness on content-based music retrieval. In addition, extensive experimental study illustrates its effectiveness and robustness against various kinds of audio alteration
acoustic signal processing, audio databases, content-based retrieval, learning (artificial intelligence), multilayer perceptrons, multimedia databases, music, pattern classification, principal component analysis, PCA, audio alteration, content-based mus
Databases and Information Systems | Numerical Analysis and Scientific Computing
Data Management and Analytics
IEEE Transactions on Multimedia
SHEN, Jialie; Shepherd, John; and Ahh, Ngu.
Efficient Content Based Music Retrieval with Multiple Acoustic Feature Composition. (2006). IEEE Transactions on Multimedia. 8, (6), 1179-1189. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/128