Skip to main content
SpringerLink
Log in

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

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Alexandre E, Cuadra L, Alvarez L, Rosa-Zurera M (2007) NN-based automatic sound classifier for digital hearing aids. In: IEEE Int. Symposium on Intelligent Signal Processing. Alcala de Henares, Spain

    Google Scholar 

  2. Alexandre E, Cuadra L, Alvarez L, Utrilla M (2007) Exploring the feasibility of a two-layer NN-based sound classifier for hearing aids. In: Proc. EUSIPCO 2007, Poznań, Poland, 3–7 September 2007

  3. Alexandre E, Cuadra L, Rosa M, Lopez-Ferreras F (2007) Feature selection for sound classification in hearing aids through restricted search driven by genetic algorithms. IEEE Trans Audio, Speech and Languagge Processing 15(8):2249–2256

    Article  Google Scholar 

  4. Alexandre E, Cuadra L, Rosa M, López-Ferreras F (2008) Speech/nonspeech classification in hearing aids driven by tailored neural networks. In: Prasad B, Prassana SM (eds) Speech, audio, image and biomedical signal processing using neural networks. Springer, Berlin, pp 145–167

    Chapter  Google Scholar 

  5. Alexandre E, Rosa-Zurera M, Cuadra L, Gil-Pita R (2006) Application of fisher linear discriminant analysis to speech/music classification. In: Proceedings of the 120th audio engineering society convention, vol 2. Paris, France, pp 1666–1669

  6. Benesty J, Makino S, Chen J (2005) Speech enhancement. Springer, Berlin, ISBN 354024039X

    Google Scholar 

  7. Büchler M (2002) Algorithms for sound classification in hearing instruments. PhD Thesis, Swiss Federal Institute of Technology, Zurich

  8. Büchler M, Allegro S, Launer S, Dillier N (2005) Sound classification in hearing aids inspired by auditory scene analysis. EURASIP J Appl Signal Process 18:2991–3002

    Google Scholar 

  9. Chang WC, Alvin Su WY, Chunghsin Y, Roebel A, Rodet X (2008) Multiple-F0 tracking based on a high-order HMM model. In: Proc. of the 11th int. conference on digital audio effects (DAFx-08), Espoo, Finland, 1–4 September 2008

  10. Cheveigne A, Kawahara H (2002) YIN, a fundamental frequency estimator for speech and music. J Acoust Soc Am (JASA) 111(4):1917–1930

    Article  Google Scholar 

  11. Cuadra L, Alexandre E, Gil-Pita R, Vicen R, Álvarez L (2009) Influence of acoustic feedback on the learning strategies of neural network-based sound classifiers in digital hearing aids. EURASIP J Appl Signal Process. doi:10.1155/2009/465189

    Google Scholar 

  12. Dong R, Hermann D, Cornu E, Chau E (2007) Low-power implementation of an HMM-based sound environment classification algorithm for hearing aid application. Proc. EUSIPCO 2007, Poznań, Poland, 3–7 September 2007

  13. El-Maleh K, Klein M, Petrucci G, Kabal P (2000) Speech/music discrimination for multimedia applications. In: Proc. IEEE ICASSP’2000, vol 6, pp 2445–2448

  14. Gil-Pita R, Alexandre E, Cuadra L, Vicen R, Rosa-Zurera M (2009) Analysis of the effects of finite precision in neural network-based sound classifiers for digital hearing aids. EURASIP J Appl Signal Process. doi:10.1155/2009/456945

    Google Scholar 

  15. Harb H, Chen L (2003) Robust speech music discrimination using spectrum’s first order statistics and neural networks. In: Proc. IEEE int. symp. on signal processing and its applications, vol 2. pp 125–128

  16. Keidser G (1995) The relationships between listening conditions and alterative amplification schemes for multiple memory hearing aids. Ear Hear 16:575–586

    Article  Google Scholar 

  17. Keidser G (1996) Selecting different amplification for different listening conditions. J Am Acad Audiol 7:92–104

    Google Scholar 

  18. Klapuri A (2003) Multiple fundamental frequency estimation by harmonicity and spectral smoothness. IEEE Trans Speech Audio Process 11(6):804–816

    Article  Google Scholar 

  19. Klapuri A (2008) Multipitch analysis of polyphonic music and speech signals using an auditory model. IEEE Trans. Audio, Speech and Language Processing 16(2):255–266

    Article  Google Scholar 

  20. Le Roux J, Kameoka H, Ono N, de Cheveigne A, Sagayama S (2007) Single and multiple F 0 contour estimation through parametric spectrogram modeling of speech in noisy environments. IEEE Transactions on Audio, Speech and Language Processing 15(4):1135–1145

    Article  Google Scholar 

  21. Luo F, Nehorai A (2006) Recent developments in signal processing for digital hearing aids. IEEE Signal Process Mag 23(5):103–106

    Article  Google Scholar 

  22. Moore BC (1989) An introduction to the psychology of hearing, 3rd edn. Academic, New York

    Google Scholar 

  23. Nordqvist P, Leijon A (2004) An efficient robust sound classification algorithm for hearing aids. J Acoust Soc Am 115(6):3033–3041

    Article  Google Scholar 

  24. On Semiconductor (2004) Toccata plus flexible DSP system for hearing aids. http://www.amis.com/products/dsp/toccata_plus.html

  25. On Semiconductor (2010) http://www.onsemi.com

  26. Quinlan JR (1993) C4.5: programs for machine learning. Morgan Kaufmann Publishers

  27. Quinlan JR (1996) Improved use of continuous attributes in C4.5. J Artif Intell Res 4:77–90

    MATH  Google Scholar 

  28. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):257–286

    Article  Google Scholar 

  29. Rabiner LR, Juang BH (1986) An introduction to hidden Markov models. IEEE ASSP Mag 3(1):4–16

    Article  Google Scholar 

  30. Rohdenburg T, Hohmann V, Kollmeier B (2007) Robustness analysis of binaural hearing aid beamformer algorithms by means of objective perceptual quality measures. In: IEEE workshop on applications of signal processing to audio and acoustics. New Paltz, NY, USA, 21–24 October 2007

  31. Ruiz-Reyes N, Vera-Candeas P, Muñoz JE, Garca-Galán S, Cañadas FJ (2009) New speech/music discrimination approach based on fundamental frequency estimation. Multimedia Tools and Applications, vol 41. Springer, pp 253–286

  32. Scheirer E, Slaney M (1997) Construction and evaluation of a robust multifeature speech/music discriminator. In: Proc. IEEE ICASSP’97. Munich, Germany, pp 1331–1334

    Google Scholar 

  33. Vera-Candeas P, Cañadas-Quesada FJ, Alexandre E, Rosa M, Ruiz-Reyes N (2008) Musical-inspired features for automatic sound classification in digital hearing aids. In: 124th Audio Engineering Society Convention, Amsterdam, The Netherlands

    Google Scholar 

Download references

Acknowledgements

This work was supported by FEDER, the Spanish Ministry of Education and Science under Project TEC2006-13883-C04-03 and the Andalusian Council under project P07-TIC-02713. We would like to thank E. Alexandre for sharing with us the database designed for digital hearing aid applications.

Author information

Authors and Affiliations

  1. Department of Telecommunication Engineering, University of Jaén, Polytechnic School, Linares, Jaén, Spain

    Pablo Cabañas Molero, Nicolas Ruiz Reyes & Pedro Vera Candeas

  2. Department of Signal Theory and Communications, University of Alcalá, Polytechnic School, Alcalá de Henares, Madrid, Spain

    Saturnino Maldonado Bascon

Authors
  1. Pablo Cabañas Molero
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nicolas Ruiz Reyes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pedro Vera Candeas
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Saturnino Maldonado Bascon
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicolas Ruiz Reyes.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Cabañas Molero, P., Ruiz Reyes, N., Vera Candeas, P. et al. Low-complexity F0-based speech/nonspeech discrimination approach for digital hearing aids. Multimed Tools Appl 54, 291–319 (2011). https://doi.org/10.1007/s11042-010-0523-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-010-0523-1

Keywords

Search

Navigation