Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm
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 appr...
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| Format: | Article |
| Language: | English |
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Wiley
2015-06-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2015/176091 |
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| author | Gi Pyo Nam Kang Ryoung Park |
| author_facet | Gi Pyo Nam Kang Ryoung Park |
| author_sort | Gi Pyo Nam |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ddecba92ba554785b70fa4b67c7411a9 |
| institution | Kabale University |
| issn | 1550-1477 |
| language | English |
| publishDate | 2015-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-ddecba92ba554785b70fa4b67c7411a92025-02-03T06:45:41ZengWileyInternational Journal of Distributed Sensor Networks1550-14772015-06-011110.1155/2015/176091176091Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping AlgorithmGi Pyo Nam0Kang Ryoung Park1 Department of Electronics Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Republic of Korea Division of Electronics and Electrical Engineering, Dongguk University, 26 Pil-dong 3-ga, Jung-gu, Seoul 100-715, Republic of KoreaWe 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.https://doi.org/10.1155/2015/176091 |
| spellingShingle | Gi Pyo Nam Kang Ryoung Park Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm International Journal of Distributed Sensor Networks |
| title | Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm |
| title_full | Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm |
| title_fullStr | Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm |
| title_full_unstemmed | Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm |
| title_short | Fast Query-by-Singing/Humming System That Combines Linear Scaling and Quantized Dynamic Time Warping Algorithm |
| title_sort | fast query by singing humming system that combines linear scaling and quantized dynamic time warping algorithm |
| url | https://doi.org/10.1155/2015/176091 |
| work_keys_str_mv | AT gipyonam fastquerybysinginghummingsystemthatcombineslinearscalingandquantizeddynamictimewarpingalgorithm AT kangryoungpark fastquerybysinginghummingsystemthatcombineslinearscalingandquantizeddynamictimewarpingalgorithm |