Evaluation of sound classification algorithms for hearing aid applications

Automatic program switching has been shown to be greatly beneficial for hearing aid users. This feature is mediated by a sound classification system, which is traditionally implemented using simple features and heuristic classification schemes, resulting in an unsatisfactory performance in complex auditory scenarios. In this study, a number of experiments are conducted to systematically assess the impact of more sophisticated classifiers and features on automatic acoustic environment classification performance. The results show that advanced classifiers, such as Hidden Markov Model (HMM) or Gaussian Mixture Model (GMM), greatly improve classification performance over simple classifiers. This change does not require a great increase of computational complexity, provided that a suitable number (5 to 7) of low-level features are carefully chosen. These findings indicate that advanced classifiers can be feasible in hearing aid applications.