In a piece of music, repeating patterns can be easily identified by human beings. Theoretically, similarities between repeating patterns and non-repeating patterns should be different. In this paper, we study similarities of patterns based on fingerprint features. According to the analysis results, we also present a relevant method to detect repeating patterns. Evaluations on some of familiar songs indicate that our method is promising.
LiuC.C.HsuJ.L. and ChenA.L., Efficient theme and non-trivial repeating pattern discovering in music databases, In Proc 15th International Conference on IEEE, Sydney (1999), 14–21.
2.
WangL.ChngE.S. and LiH., A tree-construction search approach for multivariate time series motifs discovery, Pattern Recognition Letters31(9) (Jul 2010), 869–875.
3.
LinJ.KeoghE.LonardiS. and PatelP., Finding motifs in time series, In Proc the Second Workshop on Temporal Data Mining, Edmonton, Alberta, Canada (2002), 53–68.
4.
ChiuB.KeoghE. and LonardiS., Probabilistic discovery of time series motifs, In Proc 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington (2003), 493–498.
5.
LuL.WangM. and ZhangH.J., Repeating pattern discovery and structure analysis from acoustic music data, In Proc 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, New York (2003), 275–282.
6.
PaulusJ. and KlapuriA., Music structure analysis by finding repeated parts, In Proc 1st ACM workshop on Audio and Music Computing Multimedia, California (2006), 59–68.
7.
WeiC. and VercoeB., Structural analysis of musical signals for indexing and thumbnailing, In Proc 2003 Joint Conference on IEEE in Digital Libraries, Texas (2003), 27–34.
8.
AucouturierJ.J. and SandlerM., Finding repeating patterns in acoustic musical signals: Applications for audio thumb nailing, In Proc AES22 International Conference on Virtual, Synthetic and Entertainment Audio, Espoo (2002), 412–421.
9.
HsuJ.L.LiuC.C. and ChenA.L., Efficient repeating pattern finding in music databases, In Proc the Seventh International Conference on Information and Knowledge Management, ACM, Washington (1998), 281–288.
10.
HsuJ.L.Chin LiuC. and Arbee ChenL.P., Discovering nontrivial repeating patterns in music data, In Proc IEEE Transactions on Multimedia, New York (2001), 311–325.
11.
ChiuS.C. et al., Mining polyphonic repeating patterns from music data using bit-string based approaches, In Proc ICME 2009, New York (2009), 1170–1173.
12.
WangC.LiJ. and ShiS., N-gram inverted index structures on music data for theme mining and content-based information retrieval, Pattern Recognition Letters27(5) (Apr 2006), 492–503.
13.
DarrellC., Discovery of distinctive patterns in music, Intelligent Data Analysis, Intelligent Data Analysis14 (Oct 2010), 547–554.
14.
FooteJ., Automatic audio segmentation using a measure of audio novelty, In Proc IEEE International Conference on Multimedia and Expo, New York (2000), 452–455.
15.
HaitsmaJ. and KalkerT., Highly robust audio fingerprinting system, In Proc 3rd International Conference on Music Information Retrieval (ISMIR 2002), Paris (2002), 107–115.
16.
CanoP., Content-based audio search: From fingerprinting to semantic audio retrieval, Doctoral Thesis, Fabra University, 2006.
17.
MitrovićD.ZeppelzauerM. and BreitenederC., Features for content-based audio retrieval, Advances in Computers78 (Mar 2010), 71–150.