A Computationally Efficient Sound Environment Classifier for Hearing Aids

A computationally efficient system for sound environment classification in digital hearing aids is presented in this paper. The goal is to automatically classify three different listening environments: ‘speech’, ‘music’, and ‘noise’. The system is designed considering the computational limitations found in such devices. The proposed algorithm is based on a novel set of heuristically designed features inspired in the Mel Frequency Cepstral Coefficients. Experiments carried out with real signals demonstrate that the three listening environments can be robustly classified with the proposed system, obtaining low error rates when using a small part of the total computational resources of the DSP of the device. This study demonstrates that the proposed system can be implemented with the available resources in stateof- the-art digital hearing aids.