Benchmarking Functional Link Expansions for Audio Classification Tasks

  • Simone Scardapane
  • Danilo Comminiello
  • Michele Scarpiniti
  • Raffaele Parisi
  • Aurelio Uncini
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 54)

Abstract

Functional Link Artificial Neural Networks (FLANNs) have been extensively used for tasks of audio and speech classification, due to their combination of universal approximation capabilities and fast training. The performance of a FLANN, however, is known to be dependent on the specific functional link (FL) expansion that is used. In this paper, we provide an extensive benchmark of multiple FL expansions on several audio classification problems, including speech discrimination, genre classification, and artist recognition. Our experimental results show that a random-vector expansion is well suited for classification tasks, achieving the best accuracy in two out of three tasks.

Keywords

Functional links Audio classification Speech recognition 

References

  1. 1.
    Comminiello, D., Scardapane, S., Scarpiniti, M., Parisi, R., Uncini, A.: Functional link expansions for nonlinear modeling of audio and speech signals. In: Proceedings of the International Joint Conference on Neural Networks (2015)Google Scholar
  2. 2.
    Comminiello, D., Scardapane, S., Scarpiniti, M., Parisi, R., Uncini, A.: Online selection of functional links for nonlinear system identification. In: Smart Innovation, Systems and Technologies, Springer International Publishing AG, 37, pp. 39–47 (2015)Google Scholar
  3. 3.
    Comminiello, D., Scarpiniti, M., Azpicueta-Ruiz, L.A., Arenas-García, J., Uncini, A.: Functional link adaptive filters for nonlinear acoustic echo cancellation. IEEE Trans. Acoust. Speech Signal Process. 21(7), 1502–1512 (2013)Google Scholar
  4. 4.
    Ellis, D.P.W.: Classifying music audio with timbral and chroma features. In: Proceedings of the 8th International Conference on Music Information Retrieval, pp. 339–340. Austrian Computer Society (2007)Google Scholar
  5. 5.
    Fu, Z., Lu, G., Ting, K.M., Zhang, D.: A survey of audio-based music classification and annotation. IEEE Trans. Multimedia 13(2), 303–319 (2011)CrossRefGoogle Scholar
  6. 6.
    Igelnik, B., Pao, Y.H.: Stochastic choice of basis functions in adaptive function approximation and the functional-link net. IEEE Trans. Neural Netw. 6(6), 1320–1329 (1995)CrossRefGoogle Scholar
  7. 7.
    Mierswa, I., Morik, K.: Automatic feature extraction for classifying audio data. Mach. Learn. 58(2–3), 127–149 (2005)CrossRefMATHGoogle Scholar
  8. 8.
    Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading, MA (1989)MATHGoogle Scholar
  9. 9.
    Patra, J.C., Chin, W.C., Meher, P.K., Chakraborty, G.: Legendre-FLANN-based nonlinear channel equalization in wireless communication system. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), Singapore, pp. 1826–1831, Oct 2008Google Scholar
  10. 10.
    Patra, J.C., Pal, R.N., Chatterji, B.N., Panda, G.: Identification of nonlinear dynamic systems using functional link artificial neural networks. IEEE Trans. Syst. Man Cybern. Part B 29(2), 254–262 (1999)CrossRefGoogle Scholar
  11. 11.
    Scardapane, S., Comminiello, D., Scarpiniti, M., Uncini, A.: Music classification using extreme learning machines. In: 8th International Symposium on Image and Signal Processing and Analysis (ISPA), Trieste, Italy, pp. 377–381, Sep 2013Google Scholar
  12. 12.
    Scardapane, S., Comminiello, D., Scarpiniti, M., Uncini, A.: Online sequential extreme learning machine with kernels. IEEE Trans. Neural Netw. Learn. Syst. 26(9), 2214–2220 (2015). doi:10.1109/TNNLS.2014.2382094 MathSciNetCrossRefGoogle Scholar
  13. 13.
    Scardapane, S., Fierimonte, R., Wang, D., Panella, M., Uncini, A.: Distributed music classification using random vector functional-link nets. In: Proceedings of the International Joint Conference on Neural Networks (2015)Google Scholar
  14. 14.
    Scardapane, S., Wang, D., Panella, M., Uncini, A.: Distributed learning for random vector functional-link networks. Inf. Sci. 301, 271–284 (2015)Google Scholar
  15. 15.
    Turnbull, D., Elkan, C.: Fast recognition of musical genres using RBF networks. IEEE Trans. Knowl. Data Eng. 17(4), 580–584 (2005)CrossRefGoogle Scholar
  16. 16.
    Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Simone Scardapane
    • 1
  • Danilo Comminiello
    • 1
  • Michele Scarpiniti
    • 1
  • Raffaele Parisi
    • 1
  • Aurelio Uncini
    • 1
  1. 1.Department of Information Engineering, Electronics and Telecommunications (DIET)“Sapienza” University of RomeRomeItaly

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