Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
Music structure based vector space retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Audio similarity measure by graph modeling and matching
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
Musical Genre Classification Using Nonnegative Matrix Factorization-Based Features
IEEE Transactions on Audio, Speech, and Language Processing
Effective heterogeneous similarity measure with nearest neighbors for cross-media retrieval
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
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This paper proposes a new approach for the query-by-example audio retrieval, named as segment-based manifold-ranking algorithm. Our approach adopts the audio segment, instead of the whole audio, as the basic unit for the manifold-ranking process. We formulate the query-by-example audio retrieval as a manifold-ranking problem in two stages: initial ranking and re-ranking. In the initial ranking stage, we use the existing distance functions to rank all audios according to their similarity values with the query. In the re-ranking stage, each audio is divided into some segments by the detected change points, and then the segment-based manifold-ranking algorithm is employed to re-rank the initial retrieved audios. Experimental results show the proposed approach is effective to improve the ranking capability of the existing distance functions, and the audio segment is a more appropriate unit for the manifold-ranking algorithm than the whole audio.