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Semi-supervised Echo State Networks for Audio Classification

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Abstract

Echo state networks (ESNs), belonging to the wider family of reservoir computing methods, are a powerful tool for the analysis of dynamic data. In an ESN, the input signal is fed to a fixed (possibly large) pool of interconnected neurons, whose state is then read by an adaptable layer to provide the output. This last layer is generally trained via a regularized linear least-squares procedure. In this paper, we consider the more complex problem of training an ESN for classification problems in a semi-supervised setting, wherein only a part of the input sequences are effectively labeled with the desired response. To solve the problem, we combine the standard ESN with a semi-supervised support vector machine (S3VM) for training its adaptable connections. Additionally, we propose a novel algorithm for solving the resulting non-convex optimization problem, hinging on a series of successive approximations of the original problem. The resulting procedure is highly customizable and also admits a principled way of parallelizing training over multiple processors/computers. An extensive set of experimental evaluations on audio classification tasks supports the presented semi-supervised ESN as a practical tool for dynamic problems requiring the analysis of partially labeled data.

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Notes

  1. https://www.jyu.fi/hum/laitokset/musiikki/en/research/coe/materials/mirtoolbox [last visited on November 10, 2016]

  2. https://bitbucket.org/ispamm/semi-supervised-esn [last visited on November 10, 2016]

  3. http://sound.natix.org/ [last visited November 10, 2016]

  4. http://learning.eng.cam.ac.uk/carl/code/minimize/ [last accessed November 10, 2016]

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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 & Aurelio Uncini

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  1. Simone Scardapane
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  2. Aurelio Uncini
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Correspondence to Simone Scardapane.

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Scardapane, S., Uncini, A. Semi-supervised Echo State Networks for Audio Classification. Cogn Comput 9, 125–135 (2017). https://doi.org/10.1007/s12559-016-9439-z

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