期刊名称：International Journal of Distributed Sensor Networks
印刷版ISSN：1550-1329
电子版ISSN：1550-1477
出版年度：2015
卷号：2015
DOI：10.1155/2015/176091
出版社：Hindawi Publishing Corporation
摘要：We newly propose a query-by-singing/humming (QbSH) system considering both the preclassification and multiple classifier-based method by combining linear scaling (LS) and quantized dynamic time warping (QDTW) algorithm in order to enhance both the matching accuracy and processing speed. This is appropriate for the QbSH of high speed in the huge distributed server environment. This research is novel in the following three ways. First, the processing speed of the QDTW is generally much slower than the LS method. So, we perform the QDTW matching only in case that the matching distance by LS algorithm is smaller than predetermined threshold, by which the entire processing time is reduced while the matching accuracy is maintained. Second, we use the different measurement method of matching distance in LS algorithm by considering the characteristics of reference database. Third, we combine the calculated distances of LS and QDTW algorithms based on score level fusion in order to enhance the matching accuracy. The experimental results with the 2009 MIR-QbSH corpus and the AFA MIDI 100 databases showed that the proposed method reduced the total searching time of reference data while obtaining the higher accuracy compared to the QDTW.
