Lorena Álvarez-Perez

University of Alcalá, Cómpluto, Madrid, Spain

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Publications (2)0 Total impact

  • [Show abstract] [Hide abstract]
    ABSTRACT: The design of digital hearing aids able to carry out advanced functionalities (such as, for instance, classify the acoustic environment and automatically select the best amplification program for the user's comfort) exhibits a great difficulty. Since hearing aids have to work at very low clock frequency in order to minimize power consumption and maximize life battery, the number of available instructions per second is actually very small. This enforces to design efficient algorithms with a reduced number of instructions. In particular, the paper will focus on three extremely related topics: 1) The design of low-complexity features; 2) The use of automatic feature selection algorithms to optimize the performance of the classifier; and 3) The critical analysis of a variety of different classification algorithms, basically based on their complexity and performance, and determining whether o not they are feasible to be implemented.
    No preview · Conference Paper · Jan 2009
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    ABSTRACT: This paper deals with the description of a hearing aid simulation tool. This tool simulates the real behavior of digital DSP-based hearing aids with the aim of getting a very promising performance, which can be used for further design and research, and for a better fitting of the hearing impaired patient. The main parameters to program are the noise reduction techniques and the compression and feedback reduction algorithms. Also any other configuration is possible due to the access to the simulated signals in the hearing aid. So we can get a very promising performance which can be used for further design and research and for a better fitting of the hearing impaired patient. Results using a multilevel multifrequency hearing aid with real data collected from 18 patients show how the multifrequency compression techniques adapt the normal perceptible sounds to the hearing impaired patient perceiving area.
    No preview · Conference Paper · Nov 2007