Influence of acoustic feedback on the learning strategies of neural network-based sound classifiers in digital hearing aids

Authors:
Lucas Cuadra;Enrique Alexandre;Roberto Gil-Pita;Raúl Vicen-Bueno;Lorena Álvarez
Affiliations:
Departamento de Teoria de la Señal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, Alcala de Henares, Spain;Departamento de Teoria de la Señal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, Alcala de Henares, Spain;Departamento de Teoria de la Señal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, Alcala de Henares, Spain;Departamento de Teoria de la Señal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, Alcala de Henares, Spain;Departamento de Teoria de la Señal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, Alcala de Henares, Spain
Venue:
EURASIP Journal on Advances in Signal Processing - Special issue on digital signal processing for hearing instruments
Year:
2009

Citing 7
Cited 2

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Nonlinear Fisher discriminant analysis using a minimum squared error cost function and the orthogonal least squares algorithm

Neural Networks
Evaluation of an ERB frequency scale noise reduction for hearing aids: a comparative study

Speech Communication - Special issue on speech processing for hearing aids
Construction and Evaluation of a Robust Multifeature Speech/Music Discriminator

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Sound classification in hearing aids inspired by auditory scene analysis

EURASIP Journal on Applied Signal Processing
Unbiased adaptive feedback cancellation in hearing aids by closed-loop identification

IEEE Transactions on Audio, Speech, and Language Processing

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

Multimedia Tools and Applications
Enhancing the energy efficiency of wireless-communicated binaural hearing aids for speech separation driven by soft-computing algorithms

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Sound classifiers embedded in digital hearing aids are usually designed by using sound databases that do not include the distortions associated to the feedback that often occurs when these devices have to work at high gain and low gain margin to oscillation. The consequence is that the classifier learns inappropriate sound patterns. In this paper we explore the feasibility of using different sound databases (generated according to 18 configurations of real patients), and a variety of learning strategies for neural networks in the effort of reducing the probability of erroneous classification. The experimental work basically points out that the proposed methods assist the neural network-based classifier in reducing its error probability in more than 18%. This helps enhance the elderly user's comfort: the hearing aid automatically selects, with higher success probability, the program that is best adapted to the changing acoustic environment the user is facing.