Benchmarking Functional Link Expansions for Audio Classification Tasks

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


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.


Functional links Audio classification Speech recognition 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Simone Scardapane
    • 1
    Email author
  • 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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