Relevant bibliographies by topics / Query by humming

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Query by humming'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 April 2022

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Journal articles on the topic "Query by humming"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Query by humming"

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Eriksson, Madeleine. "Att sjunga en fråga. En jämförelse av tre Query-by-Humming-system och deras användare." Thesis, Högskolan i Borås, Institutionen Biblioteks- och informationsvetenskap / Bibliotekshögskolan, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hb:diva-16635.

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The aim of this study was to compare the Query-by-Humming systems Midomi, Musicline and Tunebot regarding their retrieval effectiveness. The aim was to see if there were differences between the systems but also between the user groups common users, musicians and singers. Query-by-Humming system means that the user sings a tune that the system then use to find the right melody.To compare the systems and their users, queries where collected from the different user groups and replayed for the systems. Mean Reciprocal Rank and Friedman test was used to do the comparison.The results showed that the
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Dickerson, Kyle B. "Musical Query-by-Content Using Self-Organizing Maps." BYU ScholarsArchive, 2009. https://scholarsarchive.byu.edu/etd/1795.

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The ever-increasing density of computer storage devices has allowed the average user to store enormous quantities of multimedia content, and a large amount of this content is usually music. Current search techniques for musical content rely on meta-data tags which describe artist, album, year, genre, etc. Query-by-content systems, however, allow users to search based upon the actual acoustical content of the songs. Recent systems have mainly depended upon textual representations of the queries and targets in order to apply common string-matching algorithms and are often confined to a single qu
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Skála, František. "Vyhledávání v hudebních signálech." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236513.

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This work contains overview of methods used in the area of Music Information Retrieval, mainly for purposes of searching of musical recordings. Several existing services in the areas of music identification and searching are presented and their methods for unique song identification are described. This work also focuses on possible modifications of these algorithms for searching of cover versions of songs and for the possibility of searching based on voice created examples.
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Salamon, Justin J. "Melody extraction from polyphonic music signals." Doctoral thesis, Universitat Pompeu Fabra, 2013. http://hdl.handle.net/10803/123777.

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Music was the first mass-market industry to be completely restructured by digital technology, and today we can have access to thousands of tracks stored locally on our smartphone and millions of tracks through cloud-based music services. Given the vast quantity of music at our fingertips, we now require novel ways of describing, indexing, searching and interacting with musical content. In this thesis we focus on a technology that opens the door to a wide range of such applications: automatically estimating the pitch sequence of the melody directly from the audio signal of a polyphonic music re
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Kuo, Chin-Yang, and 郭景揚. "Accelerating query by singing/humming on GPU." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/05184339080215319813.

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碩士<br>國立清華大學<br>資訊系統與應用研究所<br>101<br>A query-by-singing/humming (QBSH) system is a technique that takes the user’s humming sound as input to find the most matching song from the song database. This study intends to discuss how to improve the matching algorithm in a QBSH system using GPU parallel computing features more efficiency, and at the same time to improve the recognition rate. The current version of QBSH system discussed in this study adopts two types of matching algorithms: one method is called linear scaling (LS), and the other method is the more time-costly dynamic time warping (DTW)
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Liu, Chueh-Chih, and 劉爵至. "Query By Humming ─ Content-Based MP3 Information Retrieval." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/30994388955937211127.

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碩士<br>大同大學<br>資訊經營研究所<br>91<br>In this thesis we investigate the approach of the MP3 content-based retrieval, allowing users to query by humming to take retrieval action. In recent years, Query By Humming (QBH) has become a popular study. Scholars are engrossed in the MIDI music format. Since the prevalence of the Internet and MP3 format appeared, which has the characteristics of little files and the MP3 music quality can complete with that of CD music, raising the revolution of digital music. This thesis focuses on the MP3 music format. The key point is comparing the musical data object by th
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張廷漢. "Query By Singing/Humming using Locality Sensitive Hashing." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/48937507664939316860.

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碩士<br>國立清華大學<br>資訊工程學系<br>101<br>Abstract This paper proposes a query by singing/humming (QBSH) method based on locality sensitive hashing (LSH).The QBSH system allows a user to sing or hum a song, and the system compares this song with the songs in the database and returns the most similar song to the user. First, we perform linear scaling to the segmented pitch vector of the user-sung or hummed tune in order to maximize the performance of LSH. LSH is then used to efficiently find relatively similar song segments in the database, and the corresponding songs of these song segments are labelled
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Cheng, Chun-Lung, and 鄭群蓉. "MIDI Query-by-Humming: A Jigsaw Matching Approach." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/86036478287685361613.

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碩士<br>國立東華大學<br>應用數學系<br>101<br>Query-by-humming is a natural and convenient way to find songs without input meta information such as singer, title. While there are websites and Apps available, how the query is specifically done remains mostly known possibly due to the commercial nature of these applications. This project considers the midi query-by-humming problem, a simplified version of query-by-humming. This simplification allows us to exploit the readily available musical information such as pitch, note duration and to focus on essential components of the query system. With a suitable dat
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Zou, Ming-Xian, and 鄒銘軒. "Query by singing/humming using combination of classifiers." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/11239109509141400500.

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Jao, Yen-Chang, and 饒彥章. "Improving Linear Scaling for Query-by- Singing/Humming." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/60523640701604135061.

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碩士<br>國立清華大學<br>資訊工程學系<br>102<br>This thesis proposes an improved framework for improving both the efficiency and the effectiveness of a query by singing/humming (QBSH) system. The proposed framework is based on three methods. Method 1 uses golden section search to reduce the computation time in traditional linear scaling (LS) algorithm. Method 2 assigns different weights for rests (in both database songs and in queries) so that these rests now have less effect on computing the weighted distance. Method 3 utilizes a sorted error vector to ignore the LS distances that are overly large and only
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Book chapters on the topic "Query by humming"

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Bu, Yingyi, Raymond Chi-Wing Wong, and Ada Wai-Chee Fu. "Query by Humming." In Encyclopedia of Database Systems. Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4614-8265-9_292.

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Shasha, Dennis, and Yunyue Zhu. "Query by Humming." In High Performance Discovery in Time Series. Springer New York, 2004. http://dx.doi.org/10.1007/978-1-4757-4046-2_6.

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Bu, Yingyi, Raymond Chi-Wing Wong, and Ada Wai-Chee Fu. "Query by Humming." In Encyclopedia of Database Systems. Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-39940-9_292.

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Bu, Yingyi, Raymond Chi-Wing Wong, and Ada Wai-Chee Fu. "Query by Humming." In Encyclopedia of Database Systems. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4899-7993-3_292-2.

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Kosugi, Naoko, Hidenobu Nagata, and Tadashi Nakanishi. "Query-by-Humming on Internet." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45227-0_58.

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Pauws, Steffen. "Cubyhum: Algorithms for Query by Humming." In Philips Research. Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-94-017-0703-9_4.

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Zhang, Guanyuan, Kai Lu, and Bin Wang. "Query Reformulation Based on User Habits for Query-by-Humming Systems." In Information Retrieval Technology. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-35341-3_34.

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Nagavi, Trisiladevi C., and Nagappa U. Bhajantri. "Query by Humming System Through Multiscale Music Entropy." In Advances in Intelligent Systems and Computing. Springer India, 2014. http://dx.doi.org/10.1007/978-81-322-2009-1_17.

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Wang, Peipei, Bin Wang, and Shiying Luo. "Top-K Similarity Search for Query-By-Humming." In Web-Age Information Management. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-39958-4_16.

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Alfaro-Paredes, Edwin, Leonardo Alfaro-Carrasco, and Willy Ugarte. "Query by Humming for Song Identification Using Voice Isolation." In Lecture Notes in Computer Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-79463-7_27.

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Conference papers on the topic "Query by humming"

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Zhu, Yunyue, Dennis Shasha, and Xiaojian Zhao. "Query by humming." In the 2003 ACM SIGMOD international conference on. ACM Press, 2003. http://dx.doi.org/10.1145/872757.872866.

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Ghias, Asif, Jonathan Logan, David Chamberlin, and Brian C. Smith. "Query by humming." In the third ACM international conference. ACM Press, 1995. http://dx.doi.org/10.1145/217279.215273.

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Shaojing Zhou, Zhijun Zhao, and Ping Shi. "Humming note segmentation method in Query-by-Humming." In 2016 10th IEEE International Conference on Anti-counterfeiting, Security, and Identification (ASID). IEEE, 2016. http://dx.doi.org/10.1109/icasid.2016.7873890.

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Nam, Gi Pyo, Kang Ryoung Park, Soek-Pil Lee, Eui Chul Lee, Moo-Young Kim, and Kichul Kim. "Intelligent Query by Humming System." In Applications (ICUT). IEEE, 2009. http://dx.doi.org/10.1109/icut.2009.5405705.

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Li, Jin, Jiqing Han, Ziqiang Shi, and Jing Li. "An efficient approach to humming transcription for query-by-humming system." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5646801.

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Wang, Qiang, Zhiyuan Guo, Gang Liu, Chunguang Li, and Jun Guo. "Local alignment for query by humming." In ICASSP 2013 - 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2013. http://dx.doi.org/10.1109/icassp.2013.6638351.

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Khan, Nauman Ali, and Mubashar Mushtaq. "Open issues on query by humming." In 2011 Fourth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2011). IEEE, 2011. http://dx.doi.org/10.1109/icadiwt.2011.6041417.

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Hsuan-Huei Shih, S. S. Narayanan, and C. C. J. Kuo. "Multidimensional humming transcription using a statistical approach for query by humming systems." In 2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698). IEEE, 2003. http://dx.doi.org/10.1109/icme.2003.1221329.

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Liu, Tao, Xianglin Huang, Lifang Yang, and Pengju Zhang. "Query by Humming: Comparing Voices to Voices." In 2009 International Conference on Management and Service Science (MASS). IEEE, 2009. http://dx.doi.org/10.1109/icmss.2009.5305356.

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Krishnamoorthy, P., R. Bhatt, A. Srinivas, and S. Kumar. "Query by humming system for embedded platforms." In 2010 Annual IEEE India Conference (INDICON 2010). IEEE, 2010. http://dx.doi.org/10.1109/indcon.2010.5712695.

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