Adaptive modeling of compression hearing aids: Convergence and tracking issues

Abstract

Typical measurements of electroacoustic performance of hearing aids include frequency response, compression ratio, threshold and time constants, equivalent input noise, and total harmonic distortion. These measurements employ artificial test signals and do not relate well to perceptual indices of hearing aid performance. Speech-based electroacoustic measures provide means to quantify the real world performance of hearing aids and have been shown to correlate better with perceptual data. This paper investigates the application of system identification paradigm for deriving the speech-based measures, where the hearing aid is modeled as a linear time-varying system and its response to speech stimuli is predicted using a linear adaptive filter. The performance of three adaptive filtering algorithms, viz. the Least Mean Square (LMS), Normalized LMS, and the Affine Projection Algorithm (APA) was investigated using simulated and real digital hearing aids. In particular, the convergence and tracking behavior of these algorithms in modeling compression hearing aids was thoroughly investigated for a range of compression ratio and threshold parameters, and attack and release time constants. Our results show that the NLMS and APA algorithms are capable of modeling digital hearing aids under a variety of compression conditions, and are suitable for deriving speech-based metrics of hearing aid performance.