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Journal articles on the topic 'Query by humming'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 April 2022

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1

Dong, Yun Feng, and Bei Qi. "A New Approximate Matching Algorithm and its Application in Internet Music Search by Humming." Advanced Materials Research 433-440 (January 2012): 3662–68. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3662.

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This paper has proposed a new approximate matching algorithm—similarity matching, and use the characteristics of algorithm to establish a system of internet music search by humming. The author compared the similarity matching algorithm and dynamic time warping (DTW) algorithm, which is most commonly used to query by humming, by the system of internet music search by humming. On the two standard of the query hit ratio and query speed, we got the result that similarity matching algorithm's comprehensive efficiency is superior, is one of QBH (query by humming) algorithm, which is applicable to th

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2

Li, Hao, and Jie Yang. "Study and Exploration into Query by Humming." Advanced Materials Research 971-973 (June 2014): 1970–73. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1970.

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3

Kharat, Vyankatesh, Kalpana Thakare, and Kishor Sadafale. "A Survey on Query by Singing/Humming." International Journal of Computer Applications 111, no. 14 (2015): 39–42. http://dx.doi.org/10.5120/19608-1484.

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4

Birmingham, William, Roger Dannenberg, and Bryan Pardo. "Query by humming with the VocalSearch system." Communications of the ACM 49, no. 8 (2006): 49–52. http://dx.doi.org/10.1145/1145287.1145313.

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5

Nagavi, Trisiladevi C., and Nagappa U. Bhajantri. "Progressive Filtering Approach for Query by Humming System Through Empirical Mode Decomposition and Multiresolution Histograms." Journal of Intelligent Systems 24, no. 2 (2015): 265–75. http://dx.doi.org/10.1515/jisys-2014-0101.

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AbstractThis research work proposes an implementation of adept content-based music retrieval technique that attempts to address the demands of the rising availability of digital music. The primary objective of this research work is to balance the perilous impact of non-relevant songs through progressive filtering (PF) for query by humming (QBH) music information retrieval system. The PF is a technique of searching in manifolds for problem solving through reduced search space. A new strategy for empirical mode decomposition (EMD) analysis is adopted, and outcomes are propelled as a significant

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6

Dannenberg, Roger B., William P. Birmingham, George Tzanetakis, Colin Meek, Ning Hu, and Bryan Pardo. "The MUSART Testbed for Query-by-Humming Evaluation." Computer Music Journal 28, no. 2 (2004): 34–48. http://dx.doi.org/10.1162/014892604323112239.

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7

Wang, Qiang, Zhiyuan Guo, Jun Guo, and Gang Liu. "Optimization of Filter Combination for Query by Humming." Circuits, Systems, and Signal Processing 34, no. 10 (2015): 3373–93. http://dx.doi.org/10.1007/s00034-015-0021-9.

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8

C. Nagavi, Trisiladevi. "An Extensive Analysis of Query by Singing/Humming System Through Query Proportion." International journal of Multimedia & Its Applications 4, no. 6 (2012): 73–86. http://dx.doi.org/10.5121/ijma.2012.4606.

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9

Lai, Wen-Hsing, and Chi-Yong Lee. "Segment-based Melody Matching for Query By Singing/Humming." MATEC Web of Conferences 76 (2016): 05006. http://dx.doi.org/10.1051/matecconf/20167605006.

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10

Jin, Xiangli. "Computer Music Query by Humming Considering Subsequence Matching Algorithm." Journal of Physics: Conference Series 2037, no. 1 (2021): 012028. http://dx.doi.org/10.1088/1742-6596/2037/1/012028.

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11

Baltiysky and Nikolenko. "Query-by-humming systems: a survey and an experimental setting." SPIIRAS Proceedings, no. 7 (March 17, 2014): 75. http://dx.doi.org/10.15622/sp.7.8.

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12

Nam, Gi, and Kang Park. "Multi-Classifier Based on a Query-by-Singing/Humming System." Symmetry 7, no. 2 (2015): 994–1016. http://dx.doi.org/10.3390/sym7020994.

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13

Rocamora, Martín, Pablo Cancela, and Alvaro Pardo. "Query by humming: Automatically building the database from music recordings." Pattern Recognition Letters 36 (January 2014): 272–80. http://dx.doi.org/10.1016/j.patrec.2013.04.006.

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14

Xiao, Limin, Yao Zheng, Wenqi Tang, Guangchao Yao, and Li Ruan. "GPU Acceleration of Melody Accurate Matching in Query-by-Humming." Scientific World Journal 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/614193.

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With the increasing scale of the melody database, the query-by-humming system faces the trade-offs between response speed and retrieval accuracy. Melody accurate matching is the key factor to restrict the response speed. In this paper, we present a GPU acceleration method for melody accurate matching, in order to improve the response speed without reducing retrieval accuracy. The method develops two parallel strategies (intra-task parallelism and inter-task parallelism) to obtain accelerated effects. The efficiency of our method is validated through extensive experiments. Evaluation results sh

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15

Unal, Erdem, Elaine Chew, Panayiotis G. Georgiou, and Shrikanth S. Narayanan. "Challenging Uncertainty in Query by Humming Systems: A Fingerprinting Approach." IEEE Transactions on Audio, Speech, and Language Processing 16, no. 2 (2008): 359–71. http://dx.doi.org/10.1109/tasl.2007.912373.

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16

Kim, Kichul, Kang Ryoung Park, Sung-Joo Park, Soek-Pil Lee, and Moo Young Kim. "Robust query-by-singing/humming system against background noise environments." IEEE Transactions on Consumer Electronics 57, no. 2 (2011): 720–25. http://dx.doi.org/10.1109/tce.2011.5955213.

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17

Suzuki, Motoyuki, Akinori Ito, and Shozo Makino. "Query-by-singing/humming systems based on speech recognition technologies." Journal of the Acoustical Society of America 122, no. 5 (2007): 2962. http://dx.doi.org/10.1121/1.2942558.

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18

Suzuki, Motoyuki, Takuto Ichikawa, Akinori Ito, and Shozo Makino. "Novel Tonal Feature and Statistical User Modeling for Query-by-Humming." Journal of Information Processing 17 (2009): 95–105. http://dx.doi.org/10.2197/ipsjjip.17.95.

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19

王, 芳. "Characteristic Parameters Analysis in Query by Humming Based on Praat Software." Open Journal of Acoustics and Vibration 02, no. 02 (2014): 11–18. http://dx.doi.org/10.12677/ojav.2014.22002.

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20

Guo, Zhiyuan, Qiang Wang, Gang Liu, and Jun Guo. "A query by humming system based on locality sensitive hashing indexes." Signal Processing 93, no. 8 (2013): 2229–43. http://dx.doi.org/10.1016/j.sigpro.2012.09.006.

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21

Wang, Chung-Che, and Jyh-Shing Roger Jang. "Improving Query-by-Singing/Humming by Combining Melody and Lyric Information." IEEE/ACM Transactions on Audio, Speech, and Language Processing 23, no. 4 (2015): 798–806. http://dx.doi.org/10.1109/taslp.2015.2409735.

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22

Meek, C. J., and W. P. Birmingham. "A Comprehensive Trainable Error Model for Sung Music Queries." Journal of Artificial Intelligence Research 22 (August 1, 2004): 57–91. http://dx.doi.org/10.1613/jair.1334.

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We propose a model for errors in sung queries, a variant of the hidden Markov model (HMM). This is a solution to the problem of identifying the degree of similarity between a (typically error-laden) sung query and a potential target in a database of musical works, an important problem in the field of music information retrieval. Similarity metrics are a critical component of `query-by-humming' (QBH) applications which search audio and multimedia databases for strong matches to oral queries. Our model comprehensively expresses the types of {m error} or variation between target and query: cumula

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23

K, KARTHIKEYAN. "AN OVERVIEW OF AUDIO CLASSIFIER METHODS AND TECHNIQUES." International Journal of Advanced Research in Computer Science and Software Engineering 8, no. 4 (2018): 1. http://dx.doi.org/10.23956/ijarcsse.v8i4.591.

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A brief overview of trends and developments in the area of Content-Based Audio Indexing and Retrieval (CBAIR), during the past few years. Here we explored some limitations and constrains of existing Query by Example (QBE) and Query by Humming (QBH) CBAIR systems. We talked about different methods to represent musical objects, like feature-based representation, musical parameter-based representation; similarly retrieval strategies, like feature based retrieval as well as melody or theme based retrieval of musical objects, in this paper. Moreover, some important issues regarding to indexing and

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24

Stasiak, Bartłomiej. "Follow That Tune-Adaptive Approach to DTW-based Query-by-Humming System." Archives of Acoustics 39, no. 4 (2015): 467–76. http://dx.doi.org/10.2478/aoa-2014-0050.

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Abstract Dynamic Time Warping is a standard algorithm used for matching time series irrespective of local tempo variations. Its application in the context of Query-by-Humming interface to multimedia databases requires providing the transposition independence, which involves some additional, sometimes computationally expensive processing and may not guarantee the success, e.g., in the presence of a pitch trend or accidental key changes. The method of tune following, proposed in this paper, enables solving the pitch alignment problem in an adaptive way inspired by the human ability of ignoring t

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25

Miao, Dezhuang, Xuesong Lu, Qiwen Dong, and Daocheng Hong. "Humming-Query and Reinforcement-Learning based Modeling Approach for Personalized Music Recommendation." Procedia Computer Science 176 (2020): 2154–63. http://dx.doi.org/10.1016/j.procs.2020.09.252.

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26

Kun, She, and Chen Shu-zhen. "Implementation of an autocorrelation pitch detector in application to query by humming." Wuhan University Journal of Natural Sciences 10, no. 3 (2005): 539–44. http://dx.doi.org/10.1007/bf02831141.

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27

Unal, Erdem, S. S. Narayanan, H. H. Shih, Elaine Chew, and C. C. Jay Kuo. "Creating data resources for designing usercentric frontends for query-by-humming systems." Multimedia Systems 10, no. 6 (2005): 475–83. http://dx.doi.org/10.1007/s00530-005-0176-5.

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28

Salamon, Justin, Joan Serrà, and Emilia Gómez. "Tonal representations for music retrieval: from version identification to query-by-humming." International Journal of Multimedia Information Retrieval 2, no. 1 (2012): 45–58. http://dx.doi.org/10.1007/s13735-012-0026-0.

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29

Kotsifakos, Alexios, Isak Karlsson, Panagiotis Papapetrou, Vassilis Athitsos, and Dimitrios Gunopulos. "Embedding-based subsequence matching with gaps–range–tolerances: a Query-By-Humming application." VLDB Journal 24, no. 4 (2015): 519–36. http://dx.doi.org/10.1007/s00778-015-0387-0.

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30

Tsai, Wei-Ho, Yu-Ming Tu, and Cin-Hao Ma. "An FFT-based fast melody comparison method for query-by-singing/humming systems." Pattern Recognition Letters 33, no. 16 (2012): 2285–91. http://dx.doi.org/10.1016/j.patrec.2012.08.020.

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31

Nagavi, Trisiladevi C., and Nagappa U. Bhajantri. "Enhanced perceptual feature space with context drift information for query by humming system." International Journal of Reasoning-based Intelligent Systems 7, no. 3/4 (2015): 161. http://dx.doi.org/10.1504/ijris.2015.072942.

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32

Ruan, Li, Long Wang, LiMin Xiao, MingFa Zhu, and YiLei Wu. "A Query-by-Humming System based on Marsyas Framework and GPU Acceleration Algorithms." Applied Mathematics & Information Sciences 7, no. 1L (2013): 261–72. http://dx.doi.org/10.12785/amis/071l35.

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33

Sailer, Christian. "Using string alignment in a query‐by‐humming system for real world applications." Journal of the Acoustical Society of America 118, no. 3 (2005): 2032. http://dx.doi.org/10.1121/1.4785801.

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34

Huang, Yo-Ping, Shin-Liang Lai, and Frode Eika Sandnes. "A repeating pattern based Query-by-Humming fuzzy system for polyphonic melody retrieval." Applied Soft Computing 33 (August 2015): 197–206. http://dx.doi.org/10.1016/j.asoc.2015.04.011.

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35

Dannenberg, Roger B., William P. Birmingham, Bryan Pardo, Ning Hu, Colin Meek, and George Tzanetakis. "A comparative evaluation of search techniques for query-by-humming using the MUSART testbed." Journal of the American Society for Information Science and Technology 58, no. 5 (2007): 687–701. http://dx.doi.org/10.1002/asi.20532.

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36

Jang, Jyh-Shing Roger, and Hong-Ru Lee. "A General Framework of Progressive Filtering and Its Application to Query by Singing/Humming." IEEE Transactions on Audio, Speech, and Language Processing 16, no. 2 (2008): 350–58. http://dx.doi.org/10.1109/tasl.2007.913035.

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37

Liu, Ning-Han. "Effective Results Ranking for Mobile Query by Singing/Humming Using a Hybrid Recommendation Mechanism." IEEE Transactions on Multimedia 16, no. 5 (2014): 1407–20. http://dx.doi.org/10.1109/tmm.2014.2311326.

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38

Song, Chai-Jong, Hochong Park, Chang-Mo Yang, Sei-Jin Jang, and Seok-Pil Lee. "Implementation of a practical query-by-singing/humming (QbSH) system and its commercial applications." IEEE Transactions on Consumer Electronics 59, no. 2 (2013): 407–14. http://dx.doi.org/10.1109/tce.2013.6531124.

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39

Park, Cheong Hee. "Query by humming based on multiple spectral hashing and scaled open-end dynamic time warping." Signal Processing 108 (March 2015): 220–25. http://dx.doi.org/10.1016/j.sigpro.2014.09.024.

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40

Hashiguchi, Hiroki, Takuichi Nishimura, J. Xin Zhang, Junko Takita, and Ryuichi Oka. "Music signal spotting retrieval by a humming query using model driven path continuous dynamic program." Systems and Computers in Japan 38, no. 10 (2007): 95–104. http://dx.doi.org/10.1002/scj.10232.

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41

Nagavi, Trisiladevi C., and Nagappa U. Bhajantri. "Fast query by humming system based on complex multiscale music entropy and CMMEB Kd tree." International Journal of Grid and Utility Computing 6, no. 3/4 (2015): 159. http://dx.doi.org/10.1504/ijguc.2015.070678.

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42

You, Jinhee, Sanghyun Park, and Inbum Kim. "An efficient frequent melody indexing method to improve the performance of query-by-humming systems." Journal of Information Science 34, no. 6 (2008): 777–98. http://dx.doi.org/10.1177/0165551507087712.

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43

Nam, Gi Pyo, Kang Ryoung Park, Sung-Joo Park, Soek-Pil Lee, and Moo-Young Kim. "A new query-by-humming system based on the score level fusion of two classifiers." International Journal of Communication Systems 25, no. 6 (2010): 717–33. http://dx.doi.org/10.1002/dac.1187.

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44

Nam, Gi Pyo, and 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 11, no. 6 (2015): 176091. http://dx.doi.org/10.1155/2015/176091.

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45

Merkley, Cari. "Music Information Seeking Behaviour Poses Unique Challenges for the Design of Information Retrieval Systems." Evidence Based Library and Information Practice 5, no. 4 (2010): 90. http://dx.doi.org/10.18438/b8t621.

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Objective – To better understand music information seeking behaviour in a real life situation and to create a taxonomy relating to this behaviour to facilitate better comparison of music information retrieval studies in the future.  Design – Content analysis of natural language queries.  Setting – Google Answers, a fee based online service.  Subjects – 1,705 queries and their related answers and comments posted in the music category of the Google Answers website before April 27, 2005.  Methods – A total of 2,208 queries were retrieved from the mu

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46

Phiwma, Nattha, and Parinya Sanguansat. "An Improved Melody Contour Feature Extraction for Query by Humming." International Journal of Computer Theory and Engineering, 2010, 523–31. http://dx.doi.org/10.7763/ijcte.2010.v2.196.

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47

Qin, Jing, Hongfei Lin, and Xinyue Liu. "Query by Humming Systems Using Melody Matching Model Based on the Genetic Algorithm." Journal of Software 6, no. 12 (2011). http://dx.doi.org/10.4304/jsw.6.12.2416-2420.

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48

Ali, Nauman, and Mubashar Mushtaq. "Hybrid Query by Humming and Metadata Search System (HQMS) Analysis over Diverse Features." International Journal of Advanced Computer Science and Applications 2, no. 9 (2011). http://dx.doi.org/10.14569/ijacsa.2011.020911.

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49

Nam, Gi Pyo, Thi Thu Trang Luong, Hyun Ha Nam, Kang Ryoung Park, and Sung-Joo Park. "Intelligent query by humming system based on score level fusion of multiple classifiers." EURASIP Journal on Advances in Signal Processing 2011, no. 1 (2011). http://dx.doi.org/10.1186/1687-6180-2011-21.

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50

"A Novel Query-by-Singing/Humming Method by Estimating Matching Positions Based on Multi-layered Perceptron." KSII Transactions on Internet and Information Systems 7, no. 7 (2013): 1657–70. http://dx.doi.org/10.3837/tiis.2013.07.008.

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