It is well known that simple amplification cannot help many hearing-impaired listeners. As a consequence of this, numerous signal enhancement algorithms have been proposed for digital hearing aids. Many of these algorithms are only effective in certain environments. The ability to quickly and correctly detect elements of the auditory scene can permit the selection/parameterization of enhancement algorithms from a library of available routines.

In this work, the authors examine the real time parameterization of a frequency-domain compression algorithm which preserves formant ratios and thus enhances speech understanding for some individuals with severe sensorineural hearing loss in the 2-3 kHz range. The optimal compression ratio is dependent upon qualities of the acoustical signal.

We briefly review the frequency-compression technology and describe a Gaussian mixture model classifier which can dynamically set the frequency compression ratio according to broad acoustic categories which we call cohorts. We discuss the results of a prototype simulator which has been implemented on a general purpose computer.