InMAF: indexing music databases via multiple acoustic features
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
QUC-tree: integrating query context information for efficient music retrieval
IEEE Transactions on Multimedia - Special issue on integration of context and content
Effective music tagging through advanced statistical modeling
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Modeling concept dynamics for large scale music search
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Audio classification with low-rank matrix representation features
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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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