Multimedia Tools and Applications

, Volume 54, Issue 2, pp 291–319

Low-complexity F0-based speech/nonspeech discrimination approach for digital hearing aids


  • Pablo Cabañas Molero
    • Department of Telecommunication EngineeringUniversity of Jaén, Polytechnic School
    • Department of Telecommunication EngineeringUniversity of Jaén, Polytechnic School
  • Pedro Vera Candeas
    • Department of Telecommunication EngineeringUniversity of Jaén, Polytechnic School
  • Saturnino Maldonado Bascon
    • Department of Signal Theory and CommunicationsUniversity of Alcalá, Polytechnic School

DOI: 10.1007/s11042-010-0523-1

Cite this article as:
Cabañas Molero, P., Ruiz Reyes, N., Vera Candeas, P. et al. Multimed Tools Appl (2011) 54: 291. doi:10.1007/s11042-010-0523-1


Digital hearing aids impose strong complexity and memory constraints on digital signal processing algorithms that implement different applications. This paper proposes a low complexity approach for automatic sound classification in digital hearing aids. The proposed scheme, which operates on a frame-by-frame basis, consists of two stages: analysis stage and classification stage. The analysis stage provides a set of low-complexity signal features derived from fundamental frequency (F0) estimation. Here, F0 estimation is performed by a decimated difference function, which results in a reduced-complexity analysis stage. The classification stage has been designed with the aim of reducing the complexity while maintaining high accuracy rates. Three low-complexity classifiers have been evaluated (tree-based C4.5, 1-Nearest Neighbor (1-NN) and a Multilayer Perceptron (MLP)), the MLP being chosen because it provides the best accuracy rates and fits to the computational and memory constraints of ultra low-power DSP-based hearing aids. The classification stage is composed of a MLP classifier followed by a Hidden Markov Model (HMM), providing a good trade-off solution between complexity and classification accuracy rate. The goal of the proposed approach is to perform a robust discrimination among speech/nonspeech parts of audio signals in commercial digital hearing aids, the computational cost being a critical issue. For the experiments, an audio database including speech, music and noise signals has been used.


Fundamental frequency estimationAutomatic sound classificationDigital hearing aidsDifference functionMultilayer perceptronHidden Markov model

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© Springer Science+Business Media, LLC 2010