Skip to main content
SpringerLink
Log in

Pattern discovery and change detection of online music query streams

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Mining of music data is one of the most important problems in multimedia data mining. In this paper, two research issues of mining music data, i.e., online mining of music query streams and change detection of music query streams, are discussed. First, we proposed an efficient online algorithm, FTP-stream (Frequent Temporal Pattern mining of streams), to mine all frequent melody structures over sliding windows of music melody sequence streams. An effective bit-sequence representation is used in the proposed algorithm to reduce the time and memory needed to slide the windows. An effective list structure is developed in the FTP-stream algorithm to overcome the performance bottleneck of 2-candidate generation. Experiments show that the proposed algorithm FTP-stream only needs a half of memory requirement of original melody sequence data, and just scans the music query stream once. After mining frequent melody structures, we developed a simple online algorithm, MQS-change (changes of Music Query Streams), to detect the changes of frequent melody structures in current user-centered music query streams. Two music melody structures (set of chord-sets and string of chord-sets) are maintained and four melody structure changes (positive burst, negative burst, increasing change and decreasing change) are monitored in a new summary data structure, MSC-list (a list of Music Structure Changes). Experiments show that the MQS-change algorithm is an effective online method to detect the changes of music melody structures over continuous music query streams.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17

Similar content being viewed by others

References

  1. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Proc. VLDB, pp 487–499

  2. Babcock B, Babu S, Data M, Motwani R, Widom J (2002) Models and issues in data stream systems. In: Proc. PODS, pp 1–16

  3. Bakhmutora V, Gusev VU, Titkova TN (1997) The search for adaptations in song melodies. Comput Music J 21(1):58–67. doi:10.2307/3681219

    Article  Google Scholar 

  4. Chang JH, Lee WS (2003) Finding recent frequent itemsets adaptively over online data streams. In: Proc. KDD, pp 487–492

  5. Chang JH, Lee WS (2004) A sliding window method for finding recently frequent itemsets over online data streams. J Inform Sci Eng (JISE) 20(4):753–762

    Google Scholar 

  6. Chi Y, Wang H, Yu P, Muntz R (2004) MOMENT: maintaining closed frequent itemsets over a stream sliding window. In: Proc. ICDM, pp 59–66

  7. Dong G, Han J, Lakshmanan LVS, Pei J, Wang H, Yu PS (2003) Online mining of changes from data streams: research problems and preliminary results. In: Proc. ACM SIGMOD-MPDS

  8. Gaber MM, Zaslavsky A, Krishnaswamy S (2005) Mining data streams: a review. SIGMOD Rec 34(1):18–26

    Article  Google Scholar 

  9. Hsu J-L, Liu C-C, Chen ALP (2001) Discovering nontrivial repeating patterns in music data. IEEE Trans Multimed 3(3):311–325. doi:10.1109/6046.944475

    Article  Google Scholar 

  10. Jones GT (1974) Music theory. Harper & Row, New York

    Google Scholar 

  11. Li H-F, Lee S-Y, Shan M-K (2004) Mining frequent closed structures in streaming melody sequences. In: Proc. ICME

  12. Li H-F, Lee S-Y, Shan M-K (2005) Online mining maximal frequent structures in continuous landmark melody streams. Pattern Recognit Lett 26(11):1658–1674. doi:10.1016/j.patrec.2005.01.016

    Article  Google Scholar 

  13. Li H-F, Lee S-Y, Shan M-K (2005) Online mining changes of items over continuous append-only and dynamic data streams. J Univers Comput Sci 11(8):1411–1425

    Google Scholar 

  14. Shan M-K, Kuo F-F (2003) Music style mining and classification by melody. IEICE Trans Inform Syst E 86-D(4):655–659

    Google Scholar 

  15. Yoshitaka A, Ichikawa T (1999) A survey on content-based retrieval for multimedia databases. IEEE Trans Knowl Data Eng 11(1):81–93. doi:10.1109/69.755617

    Article  Google Scholar 

  16. http://free.napster.com/

  17. http://new.music.yahoo.com/

  18. http://www.apple.com/itunes/

  19. http://www.kkbox.com.tw

  20. http://www.music.com.tw

Download references

Acknowledgements

The authors thank the reviewers’ precious comments for improving the quality of the paper. We would like to thank Dr. Yun Chi for contributing the source codes of Moment algorithm (MomentFP). The research is supported in part by the National Science Council, Project No. NSC 96-2218-E-424-001-, Taiwan, Republic of China.

Author information

Authors and Affiliations

  1. Department of Computer Science, Kainan University, Taoyuan, Taiwan

    Hua-Fu Li

Authors
  1. Hua-Fu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hua-Fu Li.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, HF. Pattern discovery and change detection of online music query streams. Multimed Tools Appl 41, 287–304 (2009). https://doi.org/10.1007/s11042-008-0229-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-008-0229-9

Keywords

Search

Navigation