Modeling auditory scene analysis by multidimensional statistical filtering may stimulate advances in hearing-aid signal processing

Forfattere

  • Volker Hohmann Medical Physics, University of Oldenburg, D-26111Oldenburg, Germany

Resumé

‘Auditory Scene Analysis’ (ASA) denotes the ability of the human auditory system to decode information on sound sources from a superposition of sounds in an extremely robust way. ASA is closely related to the 'Cocktail-Party-Effect' (CPE), i.e., the ability of a listener to perceive speech in adverse conditions at low signal-to-noise ratios. This contribution discusses theoretical and empirical evidence suggesting that robustness of source decoding is partly achieved by exploiting redundancies that are present in the source signals. Redundancies reflect the restricted spectro-temporal dynamics of real source signals, e.g., of speech, and limit the number of possible states of a sound source. In order to exploit them, prior knowledge on the characteristics of a sound source needs to be represented in the decoder/classifier (‘expectation-driven processing’). In a proof-of-concept approach, novel multidimensional statistical ltering algorithms such as ‘particle filters’ have been shown to successfully incorporate prior knowledge on the characteristics of speech and to estimate the dynamics of a speech source from a superposition of speech sounds (Nix and Hohmann, 2007).

Referencer

Nix, J., and Hohmann, V. (2007). "Combined estimation of spectral envelopes and sound source direction of concurrent voices by multidimensional statistical lter- ing" IEEE Trans. Audio, Speech and Lang. Proc. 15(3): 995-1008.

Anemüller, J., and Kollmeier, B. (2000). “Amplitude modulation decorrelation for convolutive blind source separation,” In: P. Pajunen und J. Karhunen (Eds.), Proc. of the second int. workshop on independent component analysis and blind signal separation, Helsinki, pp. 215-220.

Parra, L., Spence, C., and de Vries, B. (1998). “Convolutive blind source separation based on multiple decorrelation,” In: IEEE Neural Networks and Signal Process- ing Workshop, Cambridge, 1998.

Elko, G. W., and Anh-Tho, Nguyen Pong (1995). “A simple adaptive rst-order dif- ferential microphone,” IEEE ASSP Workshop on Applications of Signal Process- ing to Audio and Acoustics, New Paltz, NY, USA.

Greenberg, J. E., and Zurek, P. M. (1992). “Evaluation of an adaptive beamforming method for hearing aids,” J. Acoust. Soc. Am. 91(3):1662-76.

Kompis, M., and Dillier, N. (1994). “Noise reduction for hearing aids: combin- ing directional microphones with an adaptive beamformer,” J. Acoust. Soc. Am. 96(3):1910-13.

Kates, J. M., and Weiss, M. R. (1996), “A comparison of hearing-aid array processing techniques,” J. Acoust. Soc. Am. 99(5):3138-48.

Levitt, H., Bakke, M, Kates, J.M. (1993). “Signal processing for hearing impairment, “Scand. Audiol. Suppl. 38.

Ephraim, Y., and Malah, D. (1985). “Speech enhancement using a minimum mean- square error log-spectral amplitude estimator,” IEEE Trans. Acoust., Speech, Sig. Proc. ASSP 33(2): 443-445.

Bodden, M. (1993). “Modeling human sound-source localization and the cocktail-par- ty-effect,” Acta Acustica 1:43-56.

Marzinzik, M., and Kollmeier, B. (2003). “Predicting the Subjective Quality of Noise Reduction Algorithms for Hearing Aids,” Acta acustica/Acustica, 89, p. 521-529. Nix, J., and Hohmann, V. (2006). “Sound source localization in real sound fields based
on empirical statistics of interaural parameters,” J. Acoust. Soc. Am. 119(1): 463-479.

Hohmann, V., Nix, J., Grimm, G., Wittkop, T. (2002). “Cocktail party processing based on interaural parameters,” Forum Acusticum, Sevilla, SEA, ISBN 84-87985-06-8.

Anemüller, J. (1999). “Correlated modulation: a criterion for blind source separation,” In Joint Meeting "Berlin 99" integrated 25th German Acoustics DAGA Conference, Berlin, p. 4.

Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002). “A tutorial on particle filters for online nonlinear/non-Gaussian bayesian tracking,” IEEE Transac- tions on Signal Processing, 50, 174–188.

Yderligere filer

Publiceret

2007-12-15

Citation/Eksport

Hohmann, V. (2007). Modeling auditory scene analysis by multidimensional statistical filtering may stimulate advances in hearing-aid signal processing. Proceedings of the International Symposium on Auditory and Audiological Research, 1, 11–20. Hentet fra https://proceedings.isaar.eu/index.php/isaarproc/article/view/2007-02

Nummer

Årg. 1 (2007): Auditory Signal Processing in Hearing Impaired Listeners

Sektion

2007/1. Auditory signal processing and perception

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