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Benchmarking Functional Link Expansions for Audio Classification Tasks

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Advances in Neural Networks (WIRN 2015)

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 54))

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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.

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References

  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. 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. 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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  7. Mierswa, I., Morik, K.: Automatic feature extraction for classifying audio data. Mach. Learn. 58(2–3), 127–149 (2005)

    Article  MATH  Google Scholar 

  8. Pao, Y.H.: Adaptive Pattern Recognition and Neural Networks. Addison-Wesley, Reading, MA (1989)

    MATH  Google Scholar 

  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 2008

    Google Scholar 

  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)

    Article  Google Scholar 

  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 2013

    Google Scholar 

  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

    Article  MathSciNet  Google Scholar 

  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. 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. Turnbull, D., Elkan, C.: Fast recognition of musical genres using RBF networks. IEEE Trans. Knowl. Data Eng. 17(4), 580–584 (2005)

    Article  Google Scholar 

  16. Tzanetakis, G., Cook, P.: Musical genre classification of audio signals. IEEE Trans. Speech Audio Process. 10(5), 293–302 (2002)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Information Engineering, Electronics and Telecommunications (DIET), “Sapienza” University of Rome, Via Eudossiana 18, 00184, Rome, Italy

    Simone Scardapane, Danilo Comminiello, Michele Scarpiniti, Raffaele Parisi & Aurelio Uncini

Authors
  1. Simone Scardapane
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  2. Danilo Comminiello
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  3. Michele Scarpiniti
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  4. Raffaele Parisi
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  5. Aurelio Uncini
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Corresponding author

Correspondence to Simone Scardapane .

Editor information

Editors and Affiliations

  1. Computer Science Department, University of Milano, Milano, Italy

    Simone Bassis

  2. Department of Psychology, Seconda Università di Napoli and IIASS, Caserta, Italy

    Anna Esposito

  3. Dept. of Info., Mathematics, Ele & Trans, Univ. Mediterranea of Reggio Calabria, Reggio Calabria, Italy

    Francesco Carlo Morabito

  4. Dip. Elettronica e Telecomunicazioni, Politecnico di Torino, Torino, Italy

    Eros Pasero

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Scardapane, S., Comminiello, D., Scarpiniti, M., Parisi, R., Uncini, A. (2016). Benchmarking Functional Link Expansions for Audio Classification Tasks. In: Bassis, S., Esposito, A., Morabito, F., Pasero, E. (eds) Advances in Neural Networks. WIRN 2015. Smart Innovation, Systems and Technologies, vol 54. Springer, Cham. https://doi.org/10.1007/978-3-319-33747-0_13

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  • DOI: https://doi.org/10.1007/978-3-319-33747-0_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-33746-3

  • Online ISBN: 978-3-319-33747-0

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