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In this model, it is assumed that environmental noise is negligible, so that the only input to the HA is an acoustic signal ( s). It is not unreasonable, however, to consider other components of the HA as significant contributors to internal noise as well ( Stuart, 1994).įigure Figure1 1 shows a simple model of HA internal noise. It has been suggested that the primary source of internal noise is the microphone, specifically, random motion of electrons in the conductors, air molecules, and the particles in the diaphragm ( Thompson et al., 2002). As used here, the term “processing algorithms” refers to the application of frequency- and level-dependent gain, as well as adaptive features, such as digital noise reduction (DNR), microphone directionality, and acoustic feedback control. Internal noise can arise anywhere along the HA processing path, including at the microphone, the analog-to-digital converter (ADC), the processing algorithms, the digital-to-analog converter (DAC), and the receiver.
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Internal noise is added to any external signal processed by the HA, potentially degrading the fidelity of the sound. Internal noise is not present in the acoustic input signal, but is added by the HA itself as an unintentional by-product of its design and∕or processing. With these new processing schemes, additional methods of quantifying hearing aid performance may be necessary, including the measurement of internal noise, which is currently defined as equivalent input noise ( American National Standards Institute, 2004). Many of these algorithms are adaptive, altering their processing based on an ongoing analysis of the temporal and spectral properties of incoming sound. The continual improvement of digital technology has made possible the implementation of many innovative signal processing algorithms in hearing aids (HAs). Quantifying internal noise as the variance of the output measures allows for noise to be measured under real-world processing conditions, accounts for all sources of noise, and is predictive of internal noise audibility. Changes in noise level as a function of stimulus level demonstrated that (1) generation of internal noise is not isolated to the microphone, (2) noise may be dependent on input level, and (3) certain adaptive features may contribute to internal noise. Concurrent with HA processing of a speech-like stimulus with both adaptive features (acoustic feedback cancellation, digital noise reduction, microphone directionality) enabled and disabled, internal noise was quantified for various stimulus levels as the variance across repeated trials. The present study investigated the internal noise levels of six hearing aids (HAs).
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EIN will underestimate internal noise in the case that noise is generated following amplification. Hearing aid equivalent input noise (EIN) measures assume the primary source of internal noise to be located prior to amplification and to be constant regardless of input level.