Design and Evaluation of Feedback Control Algorithms for Implantable Hearing Devices

Using a hearing device is one of the most successful approaches to partially restore the degraded functionality of an impaired auditory system. However, due to the complex structure of the human auditory system, hearing impairment can manifest itself in different ways and, therefore, its compensation can be achieved through different classes of hearing devices. Although the majority of hearing devices consists of conventional hearing aids (HAs several other classes of hearing devices have been developed. For instance, bone-conduction devices (BCDs) and cochlear implants (CIs) have successfully been used for more than thirty years. More recently, other classes of implantable devices have been developed such as middle ear implants (MEIs), implantable BCDs, and direct acoustic cochlear implants (DACIs). Most of these different classes of hearing devices rely on a sound processor running different algorithms able to compensate for the hearing impairment. Nowadays, fully digital sound processors are the norm and this allows the use of advanced algorithms to tackle the different issues a hearing device might have to compensate for. Examples of algorithms implemented in a sound processor are, among others, noise reduction, nonlinear compression, sound scene classification, binaural enhancement, and, most importantly for the scope of this thesis, feedback cancellation. In a hearing device, feedback arises when a coupling exists between the output (i. e. the loudspeaker or a different kind of actuator) and the input (i. e. the microphone). Feedback in hearing devices gives rise to different kinds of acoustic artifacts and sound degradation which can perceptually be very annoying. Therefore, several approaches to tackle this problem have been proposed. With the advent of digital hearing devices, approaches attempting to reduce feedback through adaptive algorithms have become more viable and are, nowadays, widespread. However, despite the advances of the recent years, the feedback problem in hearing devices has not yet been solved. This is due to different reasons such as, among others, the existence of different classes and kinds of hearing devices requiring specific algorithmic tailoring, the presence of strong power constraints requiring low-complexity algorithms, and the great flexibility requirements a feedback control algorithm must have in order to cope with different daily life activities and soundscapes. This thesis presents three different tasks related to the development of a feedback control strategy for a novel hearing device as follows: 1) the feedback characterization in two novel hearing devices; 2) the presentation of new algorithms for feedback control and their comparison with the state of the art; 3) the subjective and objective evaluation of the developed algorithms in terms of sound quality. The introductory chapter provides a brief description of the auditory system and of the concept of hearing loss. Subsequently, the main differences between six classes of hearing devices are explained. Finally, an account of the feedback problem in hearing devices is given by discriminating between feedback in conventional HAs and feedback in implantable hearing devices. The first part of this thesis describes the data collection and the analysis of a series of feedback characterization measurements for two novel implantable hearing devices, the Cochlear? Codacs? DACI and an early prototype of a bone conduction implant concept (BCIC). The measurements have been performed on fresh frozen cadaver heads and are used to investigate different important aspects of the feedback these two implants may experience, such as specimen-dependent behaviors, nonlinearities, and effects of structure-borne mechanical versus acoustic feedback. The second part of this thesis introduces and describes two novel adaptive feedback cancellation (AFC) algorithms providing either comparable or better performance than existing algorithms, and both based on the prediction-error method (PEM) method. The first is an all-frequency-domain method, i. e. the frequency-domain prediction-error-method-based adaptive filter (FD-PEMAF), relying solely on frequency-domain signal-processing operations. The second is a PEM-based AFC algorithm replacing the standard adaptive filter with a simplified Kalman filter, i. e. the PEM-based frequency-domain Kalman filter (PEM-FDKF). The third part of this thesis describes a study based on a subjective listening test to assess the sound-quality degradation caused by different AFC algorithms, showing a lower sound-quality degradation compared to existing algorithms, introduced by the PEM-FDKF. Additionally, the subjective listening test results are compared to the sound quality predicted by a batch of different objective measures. Finally, the contributions of this thesis are reiterated and possible future research directions are explored.