Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2009, Article ID 465189, 10 pages doi:10.1155/2009/465189 Research Article Influence of Acoustic Feedback on the Learning Strategies of Neural Network-Based Sound Classifiers in Digital Hearing Aids Lucas Cuadra (EURASIP Member), Enrique Alexandre, Roberto Gil-Pita (EURASIP Member), Ra ´ ul Vicen-Bueno, and Lorena ´ Alvarez Departamento de Teoria de la Se˜ nal y Comunicaciones, Escuela Politecnica Superior, Universidad de Alcala, 28805 Alcala de Henares, Spain Correspondence should be addressed to Lucas Cuadra, lucas.cuadra@uah.es Received 1 December 2008; Revised 4 May 2009; Accepted 9 September 2009 Recommended by Hugo Fastl 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. Copyright © 2009 Lucas Cuadra et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 1. Introduction Acoustic feedback appears when part of the conveniently amplified output signal produced by a digital hearing aid returns through the auditory canal, and enters again this device, being thus anew amplified [1–6]. Sometimes feedback may cause the hearing aid to become unstable, producing very unpleasant and irritating howls. Preventing the hearing aid from such instability enforces designers to limit the maximum gain that can be used to compensate the patient’s acoustic loss. In this regard, along with noise reduction [1, 7, 8], the topic of controlling acoustic feedback plays a key role in the design of hearing devices [1, 3–5, 9– 17]. In particular, a very extensive and clear review by A. Spriet et al. on the topic of adaptive feedback cancellation in hearing aids may be found in [3–5] for further details. However, even without reaching the limit of instability, feedback often affects negatively the performance of those hearing aids that operate with high levels of gain, causing, for instance, distortions [1, 3–5]. In this situation, a rele- vant application—whose performance may be presumably affected, and on which this paper focuses—is the one in which the hearing aid itself classifies [1, 18–23] the acoustic environment that surrounds the user, and automatically selects the amplification “program” that is best adapted to such environment (“self-adaptation”) [20–23]. Within the more general and highly relevant topic of sound classification in hearing aids [1, 18, 19], self- adaptation is currently deemed very appreciated by hearing aid users, specially by the elderly, because the “manual” approach (in which the user has to identify the acoustic surroundings and chooses the more adequate program) is extremely uncomfortable, and very often exceeds the abilities of many hearing aid users [24, 25]. Only about 25% of hearing aid recipients (a scarce 20% of those that could benefit from hearing aids) wear it because of the unpleasant whistles and/or other amplified noises the hearing instrument often produces, and in particular, when moving from one acoustic environment (e.g., speech-in- quiet) to another different one (say, for instance, a crowded restaurant) for which the active program is not suitable (the user thus hears a sudden, uncomfortable amplified noise).