A variety of algorithms intended for the new generation of hearing aids is presented in this thesis. The main contribution of this work is the hidden Markov model (HMM) approach to classifying listening environments. This method is efficient and robust and well suited for hearing aid applications. This thesis shows that several advanced classification methods can be implemented in digital hearing aids with reasonable requirements on memory and calculation resources.
A method for analyzing complex hearing aid algorithms is presented. Data from each hearing aid and listening environment is displayed in three different forms: (1) Effective temporal characteristics (Gain-Time), (2) Effective compression characteristics (Input-Output), and (3) Effective frequency response (Insertion Gain). The method works as intended. Changes in the behavior of a hearing aid can be seen under realistic listening conditions. It is possible that the proposed method of analyzing hearing instruments generates too much information for the user.
An automatic gain controlled (AGC) hearing aid algorithm adapting to two sound sources in the listening environment is presented. The main idea of this algorithm is to: (1) adapt slowly (in approximately 10 seconds) to varying listening environments, e.g. when the user leaves a disciplined conference for a multi-babble coffee-break; (2) switch rapidly(in about 100 ms) between different dominant sound sources within one listening situation, such as the change from the user's own voice to a distant speaker's voice in a quiet conference room; (3) instantly reduce gain for strong transient sounds and then quickly return to the previous gain setting; and (4) not change the gain in silent pauses but instead keep the gain setting of the previous sound source. An acoustic evaluation shows that the algorithm works as intended.
A system for listening environment classification in hearing aids is also presented. The task is to automatically classify three different listening environments: 'speech in quiet', 'speech in traffic', and 'speech in babble'. The study shows that the three listening environments can be robustly classified at a variety of signal-to-noise ratios with only a small set of pre-trained source HMMs. The measured classification hit rate was 96.7-99.5% when the classifier was tested with sounds representing one of the three environment categories included in the classifier. False alarm rates were0.2-1.7% in these tests. The study also shows that the system can be implemented with the available resources in today's digital hearing aids. Another implementation of the classifier shows that it is possible to automatically detect when the person wearing the hearing aid uses the telephone. It is demonstrated that future hearing aids may be able to distinguish between the sound of a face-to-face conversation and a telephone conversation, both in noisy and quiet surroundings. However, this classification algorithm alone may not be fast enough to prevent initial feedback problems when the user places the telephone handset at the ear.
A method using the classifier result for estimating signal and noise spectra for different listening environments is presented. This evaluation shows that it is possible to robustly estimate signal and noise spectra given that the classifier has good performance.
An implementation and an evaluation of a single keyword recognizer for a hearing instrument are presented. The performance for the best parameter setting gives 7e-5 [1/s] in false alarm rate, i.e. one false alarm for every four hours of continuous speech from the user, 100% hit rate for an indoors quiet environment, 71% hit rate for an outdoors/traffic environment and 50% hit rate for a babble noise environment. The memory resource needed for the implemented system is estimated to 1820 words (16-bits). Optimization of the algorithm together with improved technology will inevitably make it possible to implement the system in a digital hearing aid within the next couple of years. A solution to extend the number of keywords and integrate the system with a sound environment classifier is also outlined.
Stockholm: KTH , 2004. , 51 p.