Recruitment compensation based on a simplified loudness estimation model

Abstract

To compensate for the frequency-dependent, nonlinear component of the hearing loss (recruitment), multichannel dynamic compression yields advantages over broadband compression. However, two unsolved problems remain: Due to the nonlinear additivity of perceived loudness across critical bands, the compression characteristic cannot be fitted correctly for narrow-band and broadband signals likewise. Furthermore, independent compression in different frequency bands introduces artifacts, which may limit the benefit of the algorithms. To overcome the problems, a 24-channel compression algorithm was developed and tested using critical bandwidths and time constants of ∼10 ms. The compression characteristic for broadband signals of arbitrary spectral shape is calculated by a loudness estimation model based on data from normal listeners. The algorithm should provide correct gain factors for narrow-band signals as well as for broadband signals. Furthermore, it introduces a psychoacoustically motivated interaction between frequency bands that reduces the processing artifacts. The complete algorithm was implemented in real time on a multi-signal processor setup and tested with sensorineurally impaired patients. First results of speech intelligibility measurements in quiet show the capability of the algorithm to compensate for the ‘‘distortion component’’ of the hearing loss. Further results of speech quality assessments and speech intelligibility tests in noise are presented.