Audio Environment Classication for Hearing Aids using Artificial Neural Networks with Windowed Input

Excessive background noise is one of the most common complaints from hearing aid users. Background noise classification systems can be used in hearing aids to adjust the response based on the noise environment. This paper examines and compares two promising classification techniques, non-windowed artificial neural networks (ANN) and hidden Markov models (HMM), with an artificial neural network using windowed input. Results obtained show that an ANN with a windowed input gives an accuracy of up to 97.9%, which is more accurate than both the non-windowed ANN and the HMM. Overall, a windowed ANN is able to give excellent accuracy and reliability and is considered to be a good model for background noise classification in hearing aids