Audio retrieval by segment-based manifold-ranking

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.