Prototyping a Vibrato-Aware Query-By-Humming (QBH) Music Information Retrieval System for Mobile Communication Devices: Case of Chromatic Harmonica

Authors
Dariush Alimohammadi
Keyvan Borna
Kharazmi University
Abstract
Background and Aim: The current research aims at prototyping query-by-humming music information retrieval systems for smart phones.

Methods: This multi-method research follows simulation technique from mixed models of the operations research methodology, and the documentary research method, simultaneously. Two chromatic harmonica albums comprised the research population. To achieve the purpose of research, 24 homophonic tracks were splitted by using Helium Audio Splitter software. The splits were processed by Sonic Visualiser software; and 168 XML documents were produced. On the other hand, 4 research participants hummed and recorded splits. Hummed tracks were converted by using AMR to MP3 Converter software, processed by Sonic Visualiser, and resulted in 672 XML documents. MATLAB software was learned by the first group of XML documents (168), and then, processed the second group of XML documents (672) for providing desirable outputs. Outputs were compared by using Image Comparer software.

Results: Findings indicated a high degree of similarity (99 %) between outputs of two groups of XML documents. It has also been found that the gender and the music skill do not have any impact on the results.

Conclusion: It could be acknowledged that designing query-by-humming systems based on converting audio to XML documents, and document matching, is an appropriate strategy towards developing music retrieval applications for smart phones.
Keywords
Simulation
Query-By-Humming Music Information Retrieval System
Vibrato
Mobile Communication Device
Chromatic Harmonica
Arjannikov, T. (2013). Re: MIR and Harmonica. E-mail to Dariush Alimohammadi. 6 November 2013.
Brown, J. C., and Vaughn, K. (1993). Pitch center of musical sounds with vibrato. Journal of the Acous-tical Society of America, 94 (3): 1860.
Brown, S. F. (1991). Determination of Location of Pitch within a Musical Vibrato. Bulletin of the Council for Research in Music Education, 108: 15-30.
Chen, R. (2010). Fight Against Variant Tempo: Query by Humming and Clapping. Presented at the Elev-enth International Society for Music Information Retrieval Conference (ISMIR 2010), Utrecht, Neth-erlands. Retrieved January 3, 2018. from http://ismir2010.ismir.net/proceedings/late-breaking-demo-10.pdf.
Chmel, F. (1389a). Master Class: Second Part. Trans-lated by Delta Frik. Harmonica. Retrieved January 3, 2018. http://iranharmonica.ir/Articles.php? ID=0604. [Persian]
Chmel, F. (1389b). Master Class: Third Part. Trans-lated by Delta Frik. Harmonica. Retrieved January 3, 2018. http://www.iranharmonica.ir/ Articles. php?ID=0605. [Persian]
Corso, J. F., and Lewis, D. (1950). Preferred Rate and Extent of the Frequency Vibrato. Journal of the Acoustical Society of America, 22 (1): 83.
de la Bandera, C. and et al. (2011). Humming Meth-od for Content-based Music Information Retrieval. Proceedings of the Twelfth International Society for Music Information Retrieval Conference, (IS-MIR 2011), Miami (Florida), United States of America, pp. 49-54. Retrieved January 3, 2018. http://ismir2011.ismir.net/papers/PS1-2.pdf.
Duc, T. N. T. and et al. (2008). Continuous Pitch Con-tour as an Improvement Feature for Music Infor-mation Retrieval by Humming/Singing. In Proceed-ings of the 10th Pacific Rim International Confer-ence on Artificial Intelligence: Trends in Artificial Intelligence, Hanoi, Vietnam, pp. 1086-1091.
Gersic, T. (2005). Diatonic Key Finding in a Query by Humming System. Gersic. Retrieved January 3, 2018. http://gersic.com/pdf/gersic_keyfinding.pdf.
Haus, G., and Pollastri, E. (2001). An Audio Front End for Query-by-Humming Systems. In Proceed-ings of the Second International Society for Music Information Retrieval Conference, (ISMIR 2001), Bloomington (Indiana), United States of America, pp. 65-72. Retrieved January 3, 2018. http://pdf.aminer.org/000/439/488/an_audio_front_end_for_query_by_humming_systems.pdf.
Kao, W-T. and et al. (2013). A Two-stage Query by Singing/Humming System on GPU. Presented at the Asia-Pacific Signal and Information Pro-cessing Association Annual Summit and Confer-ence (APSIPA), Municipality, Taiwan.
Kim, J. (2010). HMM-based note onset detection of natural humming for query by humming systems. Master's Dissertation, Department of Electrical En-gineering, School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), South Korea.
Kosugi, N. (2004). A study on content-based music retrieval with humming. PhD Thesis, Keio Universi-ty, Japan. Retrieved January 3, 2018. http://www.brl.ntt.co.jp/people/nao/doc/d_final_softbook.pdf.
Ma, Y. (2010). Query By Humming Using HMM-based Approach. The Ohio State University. Re-trieved January 3, 2018. http://web.cse.ohio-state.edu/~may/Project_writeup_CSE_788J04_YiMa.pdf.
Pang, H. S., and Yoon, D. H. (2005). Automatic de-tection of vibrato in monophonic music. Pattern Recognition, 38 (7): 1135-1138.
Pardo, B., and Birmingham, W. P. (2003). Query by Humming: How good can it get? Proceedings of the Workshop on Music Information Retrieval, To-ronto, Canada, pp. 107-109. Retrieved January 3, 2018. http://www.music-ir.org/evaluation/wp3/wp3_ pardo_query.pdf.
Shih, H-H., Zhang, T., and Kuo, C.-C. J. (1999). Real-time retrieval of songs from musical databases with query-by-humming. Presented at the International Symposium for Multimedia Signal Processing, Tai-pei, Taiwan.
Suzuki, M. and et al. (2009). Novel Tonal Feature and Statistical User Modeling for Query-by-Humming. Information and Media Technologies, 4 (2): 498-508. Retrieved January 3, 2018. https://www.jstage.jst.go.jp/article/imt/4/2/4_2_498/_pdf.
Tripathy, A. K. and et al. (2009). Query by Humming System. International Journal of Recent Trends in Engineering, 2 (5): 373-379. Retrieved January 3, 2018. http://www.academypublisher.com/ijrte/vol 02/ no05/ijrte0205373379.pdf.
Unal, E. and et al. (2003). Creating data resources for designing user-centric front-ends for query by humming systems. In Proceedings of the 5th ACM SIGMM International Workshop on Multimedia In-formation Retrieval, Berkeley (California), United States of America. Retrieved January 3, 2018. http://mcl.usc.edu/wp-content/uploads/2014/01/ 200510-Creating-data-resources-for-designing-user-centric-front-ends-for-query-by-humming-systems. pdf.
Unal, E. and et al. (2005). Creating data resources for designing usercentric frontends for query-by-humming systems. Multimedia Systems, 10 (6): 475-483.
Unal, E., Narayanan, S. S., and Chew, E. (2004). A statistical approach to retrieval under user-dependent uncertainty in query-by-humming sys-tems. In Proceedings of the 6th ACM SIGMM In-ternational Workshop on Multimedia Information Retrieval, New York (New York), United States of America. pp. 113-118.
Yang, X., Chen, Q., and Wang, X. (2010). A Novel Approach Based on Fault Tolerance and Recursive Segmentation to Query by Humming. Internatonal Journal of Hybrid Information Technology, 3 (3): 1-13. Retrieved January 3, 2018. http://www. sersc.org/journals/IJHIT/vol3_no3_2010/1.pdf.
Human and Information Interaction
Volume 4, Issue 4
Winter 2018
Pages 16-26