ystyle="true"> i = 1 : k x c i x m i 2 , (9)

where x is a data point, c i is the ith cluster, and m i is the centroid of c i . A large positive value of VRC indicates that the clustering performance is superior. In this study, α and β in (5) and (6) were set to 1.5 and 0.9, respectively.

Table 1 shows the performance results, which were averaged over the results of 30 trials. It is apparent that the proposed IAP method has a longer computational time and more exemplars than the original AP method. However, the former exhibits superior clustering performance, compared with the two conventional methods. Note that the running time was calculated using a PC (CPU: i7-4770@3.4 GHz, RAM: 8.0 GB). Hence, it can be concluded that rules (5) and (6) work effectively in the original AP method.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Hamamoto, T. (2015) Structural Health Monitoring of Buildings. Transactions of Foundation Engineering & Equipment, 43, 17-20. (In Japanese)
[2] Chachada, J.S. and Kuo, C.-C.J. (2014) Environmental Sound Recognition: A Survey. SIP (2014), Vol. 3, e14, 1-15.
[3] Mitrovic, D., Zeppelzauer, M. and Breiteneder, C. (2010) Features for Content-Based Audio Retrieval. In: Advances in Computers, Vol. 78, Elsevier, Amsterdam, 71-150.
[4] Deng, J.D., Simmermacher, C. and Cranefield, S. (2008) A Study on Feature Analysis for Musical Instrument Classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 38, 429-438.
[5] Peltonen, V., Tuomi, J., Klapuri, A., Huopaniemi, J. and Sorsa, T. (2002) Computational Auditory Scene Recognition. 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Orlando, FL, 13-17 May 2002, II-1941-II-1944.
[6] Potamitis, I. and Ganchev, T. (2008) Generalized Recognition of Sound Events: Approaches and Applications. In: Tsihrintzis, G.A. and Jain, L.C., Eds., Multimedia Services in Intelligent Environments, Springer, Berlin, Heidelberg, 41-79.
[7] Wang, J.-C., Wang, J.-F., He, K.W. and Hsu, C.-S. (2006) Environmental Sound Classification Using Hybrid SVM/KNN Classifier and MPEG-7 Audio Low-Level Descriptor. International Joint Conference on Neural Networks, Vancouver, 16-21 July 2006, 1731-1735.
[8] Muhammad, G., Alotaibi, Y.A., Alsulaiman, M. and Huda, M.N. (2010) Environment Recognition Using Selected MPEG-7 Audio Features and Mel-Frequency Cepstral Coefficients. 2010 5th International Conference on Digital Telecommunications (ICDT), Athens, 13-19 June 2010, 11-16.
[9] Tsau, E., Kim, S.-H. and Kuo, C.-C.J. (2011) Environmental Sound Recognition with CELP-Based Features. 2011 10th International Symposium on Signals, Circuits and Systems (ISSCS), lasi, 30 June-1 July 2011, 1-4.
[10] Chu, S., Narayanan, S. and Kuo, C.-C.J. (2009) Environmental Sound Recognition with Time-Frequency Audio Features. IEEE Transactions on Audio, Speech, and Language Processing, 17, 1142-1158.
[11] The Color Science Association of Japan (2011) Handbook of Color Science. 3rd Edition, University of Tokyo Press, Japan. (In Japanese)
[12] Cytowic, R.E. (2003) The Man Who Tasted Shapes. MIT Press, Cambridge, MA.
[13] Nagata, N., Iwai, D., Tsusa, M., Wake, S.H. and Inokuchi, S. (2003) Non-Verbal Mapping between Sound and Color-Mapping Derived from Colored Hearing Possessors and Its Applications. IEICE, J86-A, 1219-1230. (In Japanese)
[14] Schmidt, R.O. (1986) Multiple Emitter Location and Signal Parameter Estimation. IEEE Transactions on Antennas and Propagation, 34, 276-280.
[15] Muller, M. and Ewert, S. (2011) Chroma Toolbox: Matlab Implementations for Extracting Variants of Chroma-Based Audio Features. Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), Miami, 24-28 October 2011, 215-220.
[16] http://www.pampalk.at/ma/documentation.html
[17] Frey, B.J. and Dueck, D. (2007) Clustering by Passing Messages between Data Points. Science, 315, 972-976.
[18] Wang, R., Zhang, J., Li, D., Zhang, X. and Guo, T. (2007) Adaptive Affinity Propagation Clustering. Acta Automatica Sinica, 33, 1242-1246.
[19] https://jp.mathworks.com/matlabcentral/fileexchange/18244-adaptive-affinity-propagation-clustering
[20] Calinski, T. and Harabaz, J. (1974) A Dendrite Method for Cluster Analysis. Communications in Statistics, 3, 1-27.

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